{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "from sklearn.metrics import *\n",
    "from scipy.stats import norm, skew\n",
    "from scipy import stats\n",
    "\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 数据探索"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#读取数据\n",
    "target = pd.read_csv(\"Ames_House_test.csv\")\n",
    "data = pd.read_csv(\"Ames_House_train.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Id</th>\n",
       "      <th>MSSubClass</th>\n",
       "      <th>MSZoning</th>\n",
       "      <th>LotFrontage</th>\n",
       "      <th>LotArea</th>\n",
       "      <th>Street</th>\n",
       "      <th>Alley</th>\n",
       "      <th>LotShape</th>\n",
       "      <th>LandContour</th>\n",
       "      <th>Utilities</th>\n",
       "      <th>...</th>\n",
       "      <th>PoolArea</th>\n",
       "      <th>PoolQC</th>\n",
       "      <th>Fence</th>\n",
       "      <th>MiscFeature</th>\n",
       "      <th>MiscVal</th>\n",
       "      <th>MoSold</th>\n",
       "      <th>YrSold</th>\n",
       "      <th>SaleType</th>\n",
       "      <th>SaleCondition</th>\n",
       "      <th>SalePrice</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>60</td>\n",
       "      <td>RL</td>\n",
       "      <td>65.0</td>\n",
       "      <td>8450</td>\n",
       "      <td>Pave</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Reg</td>\n",
       "      <td>Lvl</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>2008</td>\n",
       "      <td>WD</td>\n",
       "      <td>Normal</td>\n",
       "      <td>208500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>20</td>\n",
       "      <td>RL</td>\n",
       "      <td>80.0</td>\n",
       "      <td>9600</td>\n",
       "      <td>Pave</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Reg</td>\n",
       "      <td>Lvl</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>2007</td>\n",
       "      <td>WD</td>\n",
       "      <td>Normal</td>\n",
       "      <td>181500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>60</td>\n",
       "      <td>RL</td>\n",
       "      <td>68.0</td>\n",
       "      <td>11250</td>\n",
       "      <td>Pave</td>\n",
       "      <td>NaN</td>\n",
       "      <td>IR1</td>\n",
       "      <td>Lvl</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>9</td>\n",
       "      <td>2008</td>\n",
       "      <td>WD</td>\n",
       "      <td>Normal</td>\n",
       "      <td>223500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>70</td>\n",
       "      <td>RL</td>\n",
       "      <td>60.0</td>\n",
       "      <td>9550</td>\n",
       "      <td>Pave</td>\n",
       "      <td>NaN</td>\n",
       "      <td>IR1</td>\n",
       "      <td>Lvl</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>2006</td>\n",
       "      <td>WD</td>\n",
       "      <td>Abnorml</td>\n",
       "      <td>140000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>60</td>\n",
       "      <td>RL</td>\n",
       "      <td>84.0</td>\n",
       "      <td>14260</td>\n",
       "      <td>Pave</td>\n",
       "      <td>NaN</td>\n",
       "      <td>IR1</td>\n",
       "      <td>Lvl</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>12</td>\n",
       "      <td>2008</td>\n",
       "      <td>WD</td>\n",
       "      <td>Normal</td>\n",
       "      <td>250000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 81 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   Id  MSSubClass MSZoning  LotFrontage  LotArea Street Alley LotShape  \\\n",
       "0   1          60       RL         65.0     8450   Pave   NaN      Reg   \n",
       "1   2          20       RL         80.0     9600   Pave   NaN      Reg   \n",
       "2   3          60       RL         68.0    11250   Pave   NaN      IR1   \n",
       "3   4          70       RL         60.0     9550   Pave   NaN      IR1   \n",
       "4   5          60       RL         84.0    14260   Pave   NaN      IR1   \n",
       "\n",
       "  LandContour Utilities    ...     PoolArea PoolQC Fence MiscFeature MiscVal  \\\n",
       "0         Lvl    AllPub    ...            0    NaN   NaN         NaN       0   \n",
       "1         Lvl    AllPub    ...            0    NaN   NaN         NaN       0   \n",
       "2         Lvl    AllPub    ...            0    NaN   NaN         NaN       0   \n",
       "3         Lvl    AllPub    ...            0    NaN   NaN         NaN       0   \n",
       "4         Lvl    AllPub    ...            0    NaN   NaN         NaN       0   \n",
       "\n",
       "  MoSold YrSold  SaleType  SaleCondition  SalePrice  \n",
       "0      2   2008        WD         Normal     208500  \n",
       "1      5   2007        WD         Normal     181500  \n",
       "2      9   2008        WD         Normal     223500  \n",
       "3      2   2006        WD        Abnorml     140000  \n",
       "4     12   2008        WD         Normal     250000  \n",
       "\n",
       "[5 rows x 81 columns]"
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#探索数据\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Id</th>\n",
       "      <th>MSSubClass</th>\n",
       "      <th>MSZoning</th>\n",
       "      <th>LotFrontage</th>\n",
       "      <th>LotArea</th>\n",
       "      <th>Street</th>\n",
       "      <th>Alley</th>\n",
       "      <th>LotShape</th>\n",
       "      <th>LandContour</th>\n",
       "      <th>Utilities</th>\n",
       "      <th>...</th>\n",
       "      <th>ScreenPorch</th>\n",
       "      <th>PoolArea</th>\n",
       "      <th>PoolQC</th>\n",
       "      <th>Fence</th>\n",
       "      <th>MiscFeature</th>\n",
       "      <th>MiscVal</th>\n",
       "      <th>MoSold</th>\n",
       "      <th>YrSold</th>\n",
       "      <th>SaleType</th>\n",
       "      <th>SaleCondition</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1461</td>\n",
       "      <td>20</td>\n",
       "      <td>RH</td>\n",
       "      <td>80.0</td>\n",
       "      <td>11622</td>\n",
       "      <td>Pave</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Reg</td>\n",
       "      <td>Lvl</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>...</td>\n",
       "      <td>120</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>MnPrv</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>2010</td>\n",
       "      <td>WD</td>\n",
       "      <td>Normal</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1462</td>\n",
       "      <td>20</td>\n",
       "      <td>RL</td>\n",
       "      <td>81.0</td>\n",
       "      <td>14267</td>\n",
       "      <td>Pave</td>\n",
       "      <td>NaN</td>\n",
       "      <td>IR1</td>\n",
       "      <td>Lvl</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Gar2</td>\n",
       "      <td>12500</td>\n",
       "      <td>6</td>\n",
       "      <td>2010</td>\n",
       "      <td>WD</td>\n",
       "      <td>Normal</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1463</td>\n",
       "      <td>60</td>\n",
       "      <td>RL</td>\n",
       "      <td>74.0</td>\n",
       "      <td>13830</td>\n",
       "      <td>Pave</td>\n",
       "      <td>NaN</td>\n",
       "      <td>IR1</td>\n",
       "      <td>Lvl</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>MnPrv</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>2010</td>\n",
       "      <td>WD</td>\n",
       "      <td>Normal</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1464</td>\n",
       "      <td>60</td>\n",
       "      <td>RL</td>\n",
       "      <td>78.0</td>\n",
       "      <td>9978</td>\n",
       "      <td>Pave</td>\n",
       "      <td>NaN</td>\n",
       "      <td>IR1</td>\n",
       "      <td>Lvl</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>2010</td>\n",
       "      <td>WD</td>\n",
       "      <td>Normal</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1465</td>\n",
       "      <td>120</td>\n",
       "      <td>RL</td>\n",
       "      <td>43.0</td>\n",
       "      <td>5005</td>\n",
       "      <td>Pave</td>\n",
       "      <td>NaN</td>\n",
       "      <td>IR1</td>\n",
       "      <td>HLS</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>...</td>\n",
       "      <td>144</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2010</td>\n",
       "      <td>WD</td>\n",
       "      <td>Normal</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 80 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Id  MSSubClass MSZoning  LotFrontage  LotArea Street Alley LotShape  \\\n",
       "0  1461          20       RH         80.0    11622   Pave   NaN      Reg   \n",
       "1  1462          20       RL         81.0    14267   Pave   NaN      IR1   \n",
       "2  1463          60       RL         74.0    13830   Pave   NaN      IR1   \n",
       "3  1464          60       RL         78.0     9978   Pave   NaN      IR1   \n",
       "4  1465         120       RL         43.0     5005   Pave   NaN      IR1   \n",
       "\n",
       "  LandContour Utilities      ...       ScreenPorch PoolArea PoolQC  Fence  \\\n",
       "0         Lvl    AllPub      ...               120        0    NaN  MnPrv   \n",
       "1         Lvl    AllPub      ...                 0        0    NaN    NaN   \n",
       "2         Lvl    AllPub      ...                 0        0    NaN  MnPrv   \n",
       "3         Lvl    AllPub      ...                 0        0    NaN    NaN   \n",
       "4         HLS    AllPub      ...               144        0    NaN    NaN   \n",
       "\n",
       "  MiscFeature MiscVal MoSold  YrSold  SaleType  SaleCondition  \n",
       "0         NaN       0      6    2010        WD         Normal  \n",
       "1        Gar2   12500      6    2010        WD         Normal  \n",
       "2         NaN       0      3    2010        WD         Normal  \n",
       "3         NaN       0      6    2010        WD         Normal  \n",
       "4         NaN       0      1    2010        WD         Normal  \n",
       "\n",
       "[5 rows x 80 columns]"
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "target.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#删除暂时没有的Id\n",
    "data_ID = data['Id']\n",
    "target_ID = target['Id']\n",
    "\n",
    "data.drop(\"Id\", axis = 1, inplace = True)\n",
    "target.drop(\"Id\", axis = 1, inplace = True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 数据处理"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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YMRevyZNcJyjZh3J0xuJzyiSTdqMaj1QsbZxOUieO8ejt6WbgK2dz44VzD43d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\nRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a23ffd978>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#对目标值进行分析\n",
    "fig, ax = plt.subplots()\n",
    "ax.scatter(x = data['GrLivArea'], y = data['SalePrice'])\n",
    "plt.ylabel('SalePrice', fontsize=13)\n",
    "plt.xlabel('GrLivArea', fontsize=13)\n",
    "plt.show()\n",
    "\n",
    "#删除右下方两个数据的点"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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ple5KFxfM7eW+x55rapA75MAuXn09O2A8tez9HLv0vtQOSQN+vuz9ha+VQcM6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\nEZFSKfCIiEip/n+H3piVOpTSKQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a23ffd080>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#删除离散点\n",
    "data = data.drop(data[(data['GrLivArea']>4000) & (data['SalePrice']<300000)].index)\n",
    "\n",
    "fig, ax = plt.subplots()\n",
    "ax.scatter(data['GrLivArea'], data['SalePrice'])\n",
    "plt.ylabel('SalePrice', fontsize=13)\n",
    "plt.xlabel('GrLivArea', fontsize=13)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      " mu = 180932.92 and sigma = 79467.79\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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ALhDvpa+n7fFAjhOj5liexhgIqIisEZFNIvILdy9CRO4SkQ0isuHECVuluvJL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\n4wAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a240fa9e8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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OqxmYpdiUbESStnkz3HtvGCabN49tDnM4mQZuZBan8z5d8n5KJRcpNUo2IkmJ\nZll+t2Equ29YSzM1TOQaJjGKFeR3xoquXTWFjJQ2JRuRfIpKlheOaWQQj/EenZnFGTRQz3zyX7Ks\nu/qlXCjZiOyCLl3g/fedY1lAPQ2czUy6s5HuDOQKbmQq57GWnnk9p3oxUo6UbERi2ndfdphrrBdv\n8DWmUk8Dh/Ic6+nOnZxDI6N5jPyXLKsXI+VMyUYkg5aJJaUDWzmZOYymkWHcR2e28CjHchGNzORs\nNtI9r3EowUilULKRqjZkCLHWd+nPckYxiYuYSD9Wspqe3MrloWSZ/JUsDx6siS+lMinZSNXJ1mtp\nqQubOYN7GU0jQwgZYA4ncwU35bVkWTdaSjVQspGKNnAgLF3ats8cxjOMppHzmEoP3qSZGq7Nc8my\nhsek2ijZSEXp0wdefTX3fi11Zz0jmE49DRzD47xHZ+7hTBoZnZeSZSUXqXatrWcjUpLGjgWzzI+2\nJRrnOB6hkYv4X/6F2xlDNzbydW6iN68yghnM5eQ2JZr0VS3THyLVTj0bKXlNTXDeefn70e7FG5zX\nomT5v/kyDdTzOEcTt2S5d29YtSo/MYlUOiUbKUntudbSml0tWR4wAJYsyV88ItVGyUZKRr4TDISS\n5YuYyCgmxSpZVmWYSDKUbKRokkgusL1kuZ4GBhNuopnNKVlLljVDskjylGykIJJKLOlaliwvpz/f\n5/tM5sIPSpZ106RIcagaTfKitQoxs+QSzZ68TT23s5BjeIYjGMuvmMcQTmIOB/ESay65mle85oOq\nMCUakeJQz0ZyamqCr34VNm4sdiQpznE8ymgaGc4MurEpdJ3qb6LLuecyvGdPhhc7RBHZgZKNZDR2\nLPz616V1j0gv3uB8pnBJl0Y+/N5z0L07jPgy1NfD0UeHLpSIlCQlG9lBUxNcdBG8916xIwk6sJVz\n9pvDtM82wKxZsGUL1B0Hoxvh7LNDwhGRkqdkU8WammDcOFi7ttiR7Oykj7zMnHMmwaRJsHIl/Kln\nCHb0aDj00GKHJyJtpGRThUrvGkxUJfb7zXDvvdDQEK7k/4fBKafAzTfD5z8flsUUkbKkZFPBSrXn\nstN9Lc88ExJM72nw5pvQvz9cey1ceCHU5GeWZREpLiWbCtDUBOPHQ3MzdOwIW7eGa+XFurgf616W\nt9+G6dOhsREefzz0Ws44I1zsHzwYOqgqX6SSKNmUofTk0jKpbN0anpNINB06hOG3dt9t7w6PPhp6\nMTNnwqZNcNhhcNNNcO650LNnXuMVkdKhZFNmmppgzJjwOw3J9166dw8l0CNH7sJB3ngDpkwJvZjn\nopLlkSPDxX6VLItUBSWbMjPtwNBqAAAL6klEQVR+/PZEk6QePeCWW3YhyWzdCnPmhF5MqmT5uONg\n4kQ46yyVLItUGQ2Ml4CmJqitDcNUtbVhu2Xb2LHhubk5/+fv3h2mTdtxsa81a9qZaF5+Gb73vRDs\n5z4Hf/5zqFJYuhQeeQRGjVKiEalG7q6HO0cddZS31bRp7v37u5uF52nT2r7/tGnue+yx47qOnTu7\nd+mSab3H/D569MgdcyzvvON+553uQ4aEA5u5Dx3qfvfd7ps35+EEIlKqgEUe4zdWw2jt1PLaSXNz\n2IbMPYJs+3ftuvOw2Pvvtz+uDh1g27btVWn9+8P11+/iNZdsUiXL01SyLCKtMy+lya+KqK6uzhct\nWhR7/2xDWv37w/Ll8fffVQVJKulSJcsNDfDEE6Fk+cwzw8V+lSyLVB0zW+zudbn2U8+mnV55JT/t\nuyJbYsu7bCXLN98cSpZ79ChAECJSzpRs2qmmJnNPJdvoUbb9e/SAd97ZcSitc+dQDdzaZJh77BF6\nMol6/fXtJcvPP6+SZRFpN415tNP114cf/HStJYBs+99yC0yYEHopZuF50qRQIZzedsklO25PmJDQ\nkNnWrXD//fDFL0LfvvCtb4WMOHEivPZaOPExxyjRiEibqGfTTqkf+vHjwxBZTU3r10xy7Z/pc4lf\nf0n38sshoUyeHGZZ7tVLsyyLSN6oQCDS1gKBivDuu9tnWZ4/P/RWhg4NCUazLItIDFVfIGBmQ4Fb\ngI5Ag7v/qMghlY6nnw7XYaZOhbfeCuNy110XSpb79St2dCJSgSoy2ZhZR+CXwEnASuAJM5vl7kuL\nG1kRvf023HlnSDIqWRaRAqvIZAMcDbzo7i8BmNl0YBhQXcnGPUwR09iokmURKapKTTZ9gBVp2yuB\nY1ruZGZjgDEANZV0x3u2kuX6evjUp1RJJiIFV6nJJtOv6U6VEO4+AZgAoUAg6aAStXUrzJ4dLvb/\n7ndhluXjj4dvf1uzLItI0VVqslkJpF/p7gu8WqRYkpUqWZ40CVatCiXLX/86XHSRSpZFpGRUarJ5\nAjjYzA4EVgEjgC8XN6Q8evdduOeeMEw2f364uH/KKeEOUZUsi0gJqshk4+5bzOwyYDah9Hmiuy8p\ncli7TiXLIlKmKjLZALj7/cD9xY5jl2UrWa6vhxNPVMmyiJSFik02ZS1VstzQAHfdpZJlESl7Sjal\nJFPJ8rnnhhsvVbIsImVMyabYtmwJJcuNjSpZFpGKpWRTLNlKlkePhkMOKXZ0IiJ5pWRTSNlKlm+9\nFU47TSXLIlKxlGwK4emnw8X+adNCyXJtrUqWRaSqKNkkJVWy3NAAixaFXssXvhCGyVSyLCJVRskm\nn9zhL38Jw2SpkuXDDw939o8cqZJlEalaSjb5kCpZbmiAv/8d9twzlCzX10NdnUqWRaTqKdnsqosv\nDj2ZVMnylVeGkuVu3YodmYhIyVCy2VUHHqiSZRGRHJRsdtW3v13sCERESp5KokREJHFKNiIikjgl\nGxERSZySjYiIJE7JRkREEqdkIyIiiVOyERGRxCnZiIhI4szdix1DSTCz1UBzgU/bE1hT4HPmg+Iu\nLMVdWIq7bfq7e69cOynZFJGZLXL3umLH0VaKu7AUd2Ep7mRoGE1ERBKnZCMiIolTsimuCcUOoJ0U\nd2Ep7sJS3AnQNRsREUmcejYiIpI4JRsREUmckk2RmdkPzOxpM3vKzOaYWe9ixxSHmf3UzJ6LYr/H\nzPYpdkxxmNlZZrbEzLaZWcmWiaaY2VAze97MXjSz7xQ7njjMbKKZvWFmzxY7lrYws35m9pCZLYv+\nHRlX7JjiMLPdzexxM/tbFPe1xY4pE12zKTIz28vd345eXw4McPeLixxWTmZ2MvAHd99iZj8GcPeS\nX7bUzA4FtgH/BXzT3RcVOaSszKwj8HfgJGAl8ARwjrsvLWpgOZjZp4ENwBR3P6zY8cRlZgcAB7j7\nk2a2J7AYOKMM/t4GdHP3DWbWGfgLMM7dFxY5tB2oZ1NkqUQT6QaURfZ39znuviXaXAj0LWY8cbn7\nMnd/vthxxHQ08KK7v+Tu7wHTgWFFjiknd/8T8Gax42grd3/N3Z+MXq8HlgF9ihtVbh5siDY7R4+S\n+x1RsikBZna9ma0ARgJXFzuedrgIeKDYQVSgPsCKtO2VlMGPXyUws1rgE8BjxY0kHjPraGZPAW8A\nc9295OJWsikAM5tnZs9meAwDcPfx7t4PaAIuK2602+WKO9pnPLCFEHtJiBN3mbAMbSX3f6yVxsy6\nA78Bvt5i5KFkuftWdz+SMMJwtJmV3PBlp2IHUA3cfUjMXf8b+D1wTYLhxJYrbjO7ADgNGOwldPGv\nDX/vUrcS6Je23Rd4tUixVIXomsdvgCZ3/22x42krd19nZg8DQ4GSKtBQz6bIzOzgtM3TgeeKFUtb\nmNlQ4NvA6e6+qdjxVKgngIPN7EAz6wKMAGYVOaaKFV1obwSWufuNxY4nLjPrlaoGNbOuwBBK8HdE\n1WhFZma/AT5GqJBqBi5291XFjSo3M3sR2A1YGzUtLJMqujOBnwO9gHXAU+5+SnGjys7MPgfcDHQE\nJrr79UUOKSczuxP4LGHK+9eBa9y9sahBxWBm/wr8GXiG8N8jwHfd/f7iRZWbmR0B3EH4d6QDMNPd\nrytuVDtTshERkcRpGE1ERBKnZCMiIolTshERkcQp2YiISOKUbEREJHFKNlJRzKxHNIP2U2b2v2a2\nKnq9zswKOqGimR0ZlS6ntk9v78zNZrbczHrmL7o2nfvC9NnIzazBzAYUOy4pL0o2UlHcfa27HxlN\n3fFr4Kbo9ZFsv3cib8ystVk4jgQ+SDbuPsvdf5TvGArgQuCDZOPu9aU+E7KUHiUbqSYdzez2aM2P\nOdHd1pjZh83sQTNbbGZ/NrNDovb+ZjY/WrNnvpnVRO2TzexGM3sI+LGZdYvWcHnCzP5qZsOiO/6v\nA4ZHPavhUQ/hF9ExPmRhHaC/RY/jovZ7oziWmNmYXF/IzEaZ2d/N7I/Rd0sdf7KZfSltvw3Rc/fo\nuzxpZs+k5oszs1oL67js8PeJjlEHNEXfo6uZPWwZ1gIys3MtrKvylJn9l4XJITtGsTwbne+KXfjn\nJ2VMyUaqycHAL919IGH2gC9G7ROAr7n7UcA3gV9F7b8grMlyBGGi0VvTjvVRYIi7/zswnrC2z6eA\nE4CfEqZ5vxqYEfW0ZrSI5Vbgj+7+ceCTwJKo/aIojjrgcjPrke3LWFh/5VrgeMKaNwNi/A3eBc50\n909Gsd4QTdOS8e/j7ncDi4CR0fd4J0sshwLDgeOjnuRWwizmRwJ93P0wdz8cmBQjRqlAmohTqsnL\n7v5U9HoxUBvN8HsccNf231x2i56PBb4QvZ4K/CTtWHe5+9bo9cnA6Wb2zWh7d6AmRywnAudDmLEX\n+GfUfnk0pQ6ESTgPZvuUQC0dAzzs7qsBzGwGIQm2xoAfWljgbBthyYIPRe/t9PfJcax0g4GjgCei\nv2NXwnT3vwMOMrOfEyaZndOGY0oFUbKRarI57fVWwg9iB2Bd9H/juaTP7bQx7bURegE7LMpmZse0\nJTgz+yxhEsVj3X1TNHvv7m2IKd0WopGLqOfSJWofSZgX7ih3f9/MlqedI9PfJ3b4wB3ufuVOb5h9\nHDgFuBQ4m7D+kVQZDaNJVYvWK3nZzM6C8MMc/TgCPEqYaRnCj/RfshxmNvC11HCUmX0ial8P7Jnl\nM/OBS6L9O5rZXsDewFtRojkEGJQj/MeAz0YVeJ2Bs9LeW07oaUBY3bNz9Hpv4I0o0ZwA9M9xjlzf\nI/37fMnM9o++037RNa+eQAd3/w3wPcKQoVQhJRuRkEhGm9nfCNdOUousXQ6MMrOngfOAcVk+/wPC\nj/nTZvZstA3wEDAgVSDQ4jPjgBPM7BnCkNVA4EGgU3S+HxCW287K3V8Dvg8sAOYBT6a9fTvwGTN7\nnDDcluqJNQF1ZrYo+t5xpqKfDPw6VSCQJZalwFXAnCj+ucABhGG6hy2sIjkZ2KnnI9VBsz6LVAgz\nuxCoc/eSWe1VJEU9GxERSZx6NiIikjj1bEREJHFKNiIikjglGxERSZySjYiIJE7JRkREEvd/vh0i\nzNGRctsAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a218484a8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot(data['SalePrice'] , fit=norm);\n",
    "\n",
    "# Get the fitted parameters used by the function\n",
    "(mu, sigma) = norm.fit(data['SalePrice'])\n",
    "print( '\\n mu = {:.2f} and sigma = {:.2f}\\n'.format(mu, sigma))\n",
    "\n",
    "#Now plot the distribution\n",
    "plt.legend(['Normal dist. ($\\mu=$ {:.2f} and $\\sigma=$ {:.2f} )'.format(mu, sigma)],\n",
    "            loc='best')\n",
    "plt.ylabel('Frequency')\n",
    "plt.title('SalePrice distribution')\n",
    "\n",
    "fig = plt.figure()\n",
    "res = stats.probplot(data['SalePrice'], plot=plt)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#此时正态分布明显属于右态分布，整体峰值向左偏离，并且skewness较大，需要对目标值做log转换，以恢复目标值的正态性"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      " mu = 12.02 and sigma = 0.40\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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SVlFt5FRFeTn7t6yj96BhePn613F00+rdwZcKA5uPZVXbPmHCBDw9PVm8eLGT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\njuM4DfM/AgPdLauQ95EAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a2400fbe0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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kO08WzkUTpYYxgmDl2Ngqa7OASzMVkHNlxXa0mzAh9XktmcdIulHC7mzPZ/Tk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\nCcM551wknjCcc85F4gnDOedcJP8PITa6AcmuMicAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a252077f0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#We use the numpy fuction log1p which  applies log(1+x) to all elements of the column\n",
    "data[\"SalePrice\"] = np.log1p(data[\"SalePrice\"])\n",
    "\n",
    "#Check the new distribution \n",
    "sns.distplot(data['SalePrice'] , fit=norm);\n",
    "\n",
    "# Get the fitted parameters used by the function\n",
    "(mu, sigma) = norm.fit(data['SalePrice'])\n",
    "print( '\\n mu = {:.2f} and sigma = {:.2f}\\n'.format(mu, sigma))\n",
    "\n",
    "#Now plot the distribution\n",
    "plt.legend(['Normal dist. ($\\mu=$ {:.2f} and $\\sigma=$ {:.2f} )'.format(mu, sigma)],\n",
    "            loc='best')\n",
    "plt.ylabel('Frequency')\n",
    "plt.title('SalePrice distribution')\n",
    "\n",
    "#Get also the QQ-plot\n",
    "fig = plt.figure()\n",
    "res = stats.probplot(data['SalePrice'], plot=plt)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1a1df8e6d8>"
      ]
     },
     "execution_count": 89,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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PXBwRD6W2zAVOAU5O9V8s6dCq9lR6Z3PPY2ZmZtZ+nme1me68wWp7YFr1joh4W9KLwE3A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\nYZ7B9erVRJ3wf0jat+LYR9J3w5+JgKu4IG1SdXnQt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      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a1f173d68>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#进行相关系数分析\n",
    "corrmat = data.corr()\n",
    "plt.subplots(figsize=(12,9))\n",
    "sns.heatmap(corrmat, vmax=0.9, square=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "方块越白，特征相关性越高，但值得注意的是，有两个正方形小块：TotaLBsmtSF和1stFlrSF、GarageAreas和GarageCars处。这代表全部建筑面积TotaLBsmtSF与一层建筑面积1stFlrSF成强正相关，车库区域GarageAreas和车库车辆GarageCars成强正相关。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 特征工程"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# 这里直接读取老师帮我们做好的特征工程\n",
    "target = pd.read_csv(\"AmesHouse_FE_test.csv\")\n",
    "data = pd.read_csv(\"AmesHouse_FE_train.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    .dataframe thead th {\n",
       "        text-align: left;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th>Id</th>\n",
       "      <th>LotFrontage</th>\n",
       "      <th>LotArea</th>\n",
       "      <th>Street</th>\n",
       "      <th>Alley</th>\n",
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       "      <th>SimplPoolQC_nan</th>\n",
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       "      <td>-1.028509</td>\n",
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       "      <td>0.026216</td>\n",
       "      <td>0.226042</td>\n",
       "      <td>1.253123</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 345 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   Unnamed: 0    Id  LotFrontage   LotArea    Street     Alley  LotShape  \\\n",
       "0           0  1461     0.670403  0.119019  0.064327 -0.243378  0.700717   \n",
       "1           1  1462     0.699931  0.387346  0.064327 -0.243378 -1.028509   \n",
       "2           2  1463     0.493235  0.343014  0.064327 -0.243378 -1.028509   \n",
       "3           3  1464     0.611347 -0.047760  0.064327 -0.243378 -1.028509   \n",
       "4           4  1465    -0.422133 -0.552255  0.064327 -0.243378 -1.028509   \n",
       "\n",
       "   Utilities  LandSlope  OverallQual         ...          SimplGarageQual_nan  \\\n",
       "0   0.026216   0.226042    -0.694099         ...                            1   \n",
       "1   0.026216   0.226042    -0.694099         ...                            1   \n",
       "2   0.026216   0.226042    -0.694099         ...                            1   \n",
       "3   0.026216   0.226042    -0.694099         ...                            1   \n",
       "4   0.026216   0.226042     1.253123         ...                            1   \n",
       "\n",
       "   SimplGarageScore_nan  SimplHeatingQC_nan  SimplKitchenQual_nan  \\\n",
       "0                     1                   1                     1   \n",
       "1                     1                   1                     1   \n",
       "2                     1                   1                     1   \n",
       "3                     1                   1                     1   \n",
       "4                     1                   1                     1   \n",
       "\n",
       "   SimplKitchenScore_nan  SimplOverallCond_nan  SimplOverallGrade_nan  \\\n",
       "0                      1                     1                      1   \n",
       "1                      1                     1                      1   \n",
       "2                      1                     1                      1   \n",
       "3                      1                     1                      1   \n",
       "4                      1                     1                      1   \n",
       "\n",
       "   SimplOverallQual_nan  SimplPoolQC_nan  SimplPoolScore_nan  \n",
       "0                     1                1                   1  \n",
       "1                     1                1                   1  \n",
       "2                     1                1                   1  \n",
       "3                     1                1                   1  \n",
       "4                     1                1                   1  \n",
       "\n",
       "[5 rows x 345 columns]"
      ]
     },
     "execution_count": 91,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "target.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
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       "        text-align: right;\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th>LotFrontage</th>\n",
       "      <th>LotArea</th>\n",
       "      <th>Street</th>\n",
       "      <th>Alley</th>\n",
       "      <th>LotShape</th>\n",
       "      <th>Utilities</th>\n",
       "      <th>LandSlope</th>\n",
       "      <th>OverallQual</th>\n",
       "      <th>OverallCond</th>\n",
       "      <th>...</th>\n",
       "      <th>SimplGarageScore_nan</th>\n",
       "      <th>SimplHeatingQC_nan</th>\n",
       "      <th>SimplKitchenQual_nan</th>\n",
       "      <th>SimplKitchenScore_nan</th>\n",
       "      <th>SimplOverallCond_nan</th>\n",
       "      <th>SimplOverallGrade_nan</th>\n",
       "      <th>SimplOverallQual_nan</th>\n",
       "      <th>SimplPoolQC_nan</th>\n",
       "      <th>SimplPoolScore_nan</th>\n",
       "      <th>SalePrice</th>\n",
       "    </tr>\n",
       "  </thead>\n",
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       "      <td>-0.419233</td>\n",
       "      <td>...</td>\n",
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       "      <td>-0.694099</td>\n",
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       "      <td>-0.419233</td>\n",
       "      <td>...</td>\n",
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       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>223500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>0.079843</td>\n",
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       "      <td>0.064327</td>\n",
       "      <td>-0.243378</td>\n",
       "      <td>-1.028509</td>\n",
       "      <td>0.026216</td>\n",
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       "      <td>...</td>\n",
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       "      <td>140000</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>0.788515</td>\n",
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       "      <td>0.064327</td>\n",
       "      <td>-0.243378</td>\n",
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       "      <td>0.026216</td>\n",
       "      <td>0.226042</td>\n",
       "      <td>1.253123</td>\n",
       "      <td>-0.419233</td>\n",
       "      <td>...</td>\n",
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       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>250000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 345 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   Unnamed: 0  LotFrontage   LotArea    Street     Alley  LotShape  Utilities  \\\n",
       "0           0     0.227483 -0.202770  0.064327 -0.243378  0.700717   0.026216   \n",
       "1           1     0.670403 -0.086107  0.064327 -0.243378  0.700717   0.026216   \n",
       "2           2     0.316067  0.081281  0.064327 -0.243378 -1.028509   0.026216   \n",
       "3           3     0.079843 -0.091179  0.064327 -0.243378 -1.028509   0.026216   \n",
       "4           4     0.788515  0.386636  0.064327 -0.243378 -1.028509   0.026216   \n",
       "\n",
       "   LandSlope  OverallQual  OverallCond    ...      SimplGarageScore_nan  \\\n",
       "0   0.226042     1.253123    -0.419233    ...                         1   \n",
       "1   0.226042    -0.694099     1.858393    ...                         1   \n",
       "2   0.226042     1.253123    -0.419233    ...                         1   \n",
       "3   0.226042     1.253123    -0.419233    ...                         1   \n",
       "4   0.226042     1.253123    -0.419233    ...                         1   \n",
       "\n",
       "   SimplHeatingQC_nan  SimplKitchenQual_nan  SimplKitchenScore_nan  \\\n",
       "0                   1                     1                      1   \n",
       "1                   1                     1                      1   \n",
       "2                   1                     1                      1   \n",
       "3                   1                     1                      1   \n",
       "4                   1                     1                      1   \n",
       "\n",
       "   SimplOverallCond_nan  SimplOverallGrade_nan  SimplOverallQual_nan  \\\n",
       "0                     1                      1                     1   \n",
       "1                     1                      1                     1   \n",
       "2                     1                      1                     1   \n",
       "3                     1                      1                     1   \n",
       "4                     1                      1                     1   \n",
       "\n",
       "   SimplPoolQC_nan  SimplPoolScore_nan  SalePrice  \n",
       "0                1                   1     208500  \n",
       "1                1                   1     181500  \n",
       "2                1                   1     223500  \n",
       "3                1                   1     140000  \n",
       "4                1                   1     250000  \n",
       "\n",
       "[5 rows x 345 columns]"
      ]
     },
     "execution_count": 92,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 模型选择"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "y = data.SalePrice.values\n",
    "X = data.drop('SalePrice',axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [],
   "source": [
    "#将数据分割训练数据与测试数据\n",
    "from sklearn.cross_validation import train_test_split\n",
    "\n",
    "# 随机采样25%的数据构建测试样本，其余作为训练样本\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.25)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/anaconda3/lib/python3.6/site-packages/sklearn/utils/validation.py:475: DataConversionWarning: Data with input dtype int64 was converted to float64 by StandardScaler.\n",
      "  warnings.warn(msg, DataConversionWarning)\n"
     ]
    }
   ],
   "source": [
    "# 对数据的标准化\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "\n",
    "ss_X = StandardScaler()\n",
    "ss_y = StandardScaler()\n",
    "\n",
    "X_train = ss_X.fit_transform(X_train)\n",
    "X_test = ss_X.transform(X_test)\n",
    "\n",
    "y_train = ss_y.fit_transform(y_train.reshape(-1, 1))\n",
    "y_test = ss_y.transform(y_test.reshape(-1, 1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "LinearRegression coef [[ -1.06810451e-03   1.11480851e-02   6.08293643e-02   3.63235773e-02\n",
      "   -7.59079599e-03  -1.15133653e-03   1.04870118e-02   4.44693717e-03\n",
      "   -5.24403330e+10  -8.72566700e-02   1.76240444e-01   5.64665794e-02\n",
      "    2.65979767e-02  -2.16763157e+11  -4.45928574e-02  -1.33167952e-02\n",
      "    2.71320343e-04   6.75755739e-02   3.97256017e-03   7.24331233e+10\n",
      "    5.10597229e-03   2.64379598e+10   7.22138080e+10  -6.10966383e+10\n",
      "    1.21397972e-02   7.19552864e+10   8.24981680e+10   2.57653919e+10\n",
      "   -2.41344289e+11  -5.22450969e+10  -1.21351522e+10  -5.44700291e+10\n",
      "   -2.52610096e+10  -7.19127655e-02  -1.38948441e-01  -1.97102308e-01\n",
      "    7.49683380e-02   5.23701906e-02  -2.64353320e+10  -1.99437141e-02\n",
      "    6.82783127e-03   1.11620688e+10  -5.58856440e+00  -7.50131607e-02\n",
      "    3.33738327e-02   1.46445036e-02   4.85806167e-03   1.96980041e+11\n",
      "    1.75002663e+11   8.39937536e+10   1.65248309e+11  -1.07803345e-02\n",
      "   -1.12363577e-01   2.35222578e-02   6.06346130e-03   2.15354800e-01\n",
      "   -4.81634140e-02   4.77914810e-02   2.15306148e-01   2.64353320e+10\n",
      "    2.27743626e-01   1.55634880e-01   7.86385409e+10  -1.38875199e+10\n",
      "    1.54116691e+11  -3.12141310e+11  -1.94232188e+11  -3.30370705e+11\n",
      "   -2.92975688e+00   1.44946551e+00  -7.96667099e-01   2.31396689e+01\n",
      "   -7.79963732e+00   1.48454750e+01  -1.81442356e+01   6.04230499e+00\n",
      "   -1.21386085e+01  -2.82222811e+11   2.25428878e+11   1.11023176e+11\n",
      "   -5.54920192e+09   1.45808567e+09  -9.08629159e+09   1.06197739e+00\n",
      "   -4.91707802e-01   1.84033513e-01   2.99047941e+00  -7.58209229e-01\n",
      "    4.07746220e+00   2.30395746e+00  -6.55076981e-01   1.89556646e+00\n",
      "   -2.04626416e+11   8.35367941e+10   3.37852779e+11   3.71814156e+00\n",
      "   -1.33982515e+00   2.46832848e+00   3.94933058e+11   1.78804635e+11\n",
      "    2.08494743e+11   1.77566855e+11   2.86577554e+11  -1.98927300e+10\n",
      "    9.78062528e+09   9.78062528e+09   2.12932023e+10  -3.61313880e+10\n",
      "   -4.71093790e+10  -7.08102968e+10  -1.51636470e+10  -2.30773814e+10\n",
      "   -1.85459298e+10  -2.88214853e+10   5.26900434e+10  -1.23924613e+10\n",
      "   -5.61770396e+10  -1.30456827e+11  -1.59703021e+11  -3.04682671e+11\n",
      "   -9.22892126e+10  -9.22892126e+10  -9.22892126e+10  -9.22892126e+10\n",
      "   -1.30456827e+11   1.96811250e+10   2.41066107e+11   1.43345926e+11\n",
      "    5.11762755e+10   2.95737559e+10   2.82000543e+11   2.95737559e+10\n",
      "    1.51319276e+10   3.80925899e+09   3.80925899e+09   2.36573444e+10\n",
      "   -1.03756702e+11   2.60716382e+10   4.53270263e+10   3.80925899e+09\n",
      "    4.53270263e+10   3.41012881e+10   5.38463628e+09   1.31289691e+10\n",
      "    5.98002966e+10   4.39504950e+10   1.60349097e+10  -2.39788466e+10\n",
      "   -1.41381570e+10  -6.35790282e+09  -8.20047658e+09  -1.54659895e+10\n",
      "    1.03891855e+11  -2.49006215e+10  -4.26181850e+10  -9.69400429e+09\n",
      "   -4.29543613e+10   3.02249323e+10  -3.72851179e+10  -7.33810230e+09\n",
      "   -1.36650969e+10  -5.73774646e+10  -4.15772895e+10  -1.98546486e+10\n",
      "   -2.20443927e+10   1.12017915e+11   1.13391072e+11   1.93511382e+11\n",
      "    4.81731324e+10   2.39180207e+11  -1.35964700e+10  -7.70294780e+10\n",
      "   -1.23680576e+11  -1.24187396e+11  -3.35208823e+10  -1.06888578e+10\n",
      "   -1.30851172e+10   9.39385995e+08   2.10036808e+11   6.71114728e+10\n",
      "    2.22828894e+11   2.42091470e+11  -3.15086081e+10   8.68434643e+09\n",
      "    7.04024642e+10   1.55910403e+10   3.27573054e+10   1.06263284e+10\n",
      "    6.33456745e+10   8.08365334e+10  -3.91361152e+10   3.18207577e+09\n",
      "    1.54237291e+10   1.22447979e+10   7.10228205e+09  -1.00057299e+11\n",
      "    5.50646278e+09   2.47558396e+10   1.34219421e+11   4.95744827e+10\n",
      "    2.20301993e+11   3.51680819e+10   4.19757460e+10   2.00936157e+11\n",
      "    6.71815576e+10   8.75919894e+10  -4.18259739e+10  -1.11877761e+11\n",
      "   -9.90411500e+10  -8.39189916e+10  -1.65243895e+11   1.60996184e+10\n",
      "    2.29168409e+11   1.52879727e+11   1.07270291e+11   3.62944895e+10\n",
      "    2.70617807e+11  -2.31191381e+10   1.16514663e+11   5.99047504e+09\n",
      "    9.81723280e+10   3.54004481e+10   5.02871039e+10   2.31442598e+11\n",
      "    1.02911505e+11   2.50664475e+10   4.99253045e+10   1.41179438e+11\n",
      "    1.90095732e+11   9.20953105e+10   5.19398415e+10   8.99613755e+10\n",
      "    5.75419200e+10   7.92418384e+10   5.51161603e+09  -5.84406714e+10\n",
      "   -1.39079345e+11  -7.70954878e+10  -2.79874369e+11  -2.43587245e+11\n",
      "   -1.72366591e+10   2.29640085e+10   1.15924878e+11  -7.03370886e+10\n",
      "    7.12428710e+10   1.18295569e+09   2.16202344e+10   9.61225842e+10\n",
      "    1.52948255e+10   9.14869698e+10   1.52948255e+10   0.00000000e+00\n",
      "    6.82426685e+10   6.21568009e+10   4.51391084e+10   4.04312820e+10\n",
      "    4.31205074e+10   8.34207072e+10   8.73811721e+10   6.24818945e+10\n",
      "    7.96754470e+10   5.27157327e+10   5.62238349e+10   4.51391084e+10\n",
      "    0.00000000e+00  -3.75965033e+09  -1.01084934e+09  -3.02140915e+09\n",
      "   -6.85085735e+09  -4.69545071e+09  -1.00583361e+10  -6.12471889e+09\n",
      "   -8.23978921e+09  -7.84983631e+09  -5.55025064e+09  -3.62456970e+09\n",
      "   -6.20183983e+09  -1.18788128e+10  -2.84992671e+09  -7.43329997e+09\n",
      "   -5.46256908e+09  -7.37136553e+09  -8.60677273e+09  -4.69545071e+09\n",
      "   -7.37136554e+09  -6.49974066e+09  -7.55527407e+09  -4.36969091e+09\n",
      "   -5.63640223e+09  -3.18337416e+09   0.00000000e+00  -1.70055626e+11\n",
      "   -4.03984764e+10  -4.03984764e+10  -4.03984764e+10  -1.20750265e+11\n",
      "   -6.99080484e+10  -6.99080484e+10   0.00000000e+00  -8.57236590e+09\n",
      "   -3.53333157e+10  -7.76116638e+09  -3.38938335e+10  -4.49330685e+09\n",
      "   -3.67045376e+09   0.00000000e+00   1.63783081e+09   3.36891423e+08\n",
      "    6.13096755e+08   7.49150146e+08   2.44508784e+09   3.32113547e+11\n",
      "    0.00000000e+00  -3.35343798e+10  -1.05688153e+10  -8.63336270e+09\n",
      "   -1.36317469e+10  -1.21982122e+10  -1.21982122e+10  -5.43876076e+10\n",
      "   -1.05688153e+10  -6.70911862e+10   0.00000000e+00   0.00000000e+00\n",
      "    0.00000000e+00   0.00000000e+00   0.00000000e+00   0.00000000e+00\n",
      "    0.00000000e+00   0.00000000e+00   0.00000000e+00   0.00000000e+00\n",
      "    0.00000000e+00   0.00000000e+00   0.00000000e+00   0.00000000e+00\n",
      "    0.00000000e+00   0.00000000e+00   0.00000000e+00   0.00000000e+00\n",
      "    0.00000000e+00   0.00000000e+00   0.00000000e+00   0.00000000e+00]]\n",
      "LinearRegression intercept [ -1.74179956e-05]\n"
     ]
    }
   ],
   "source": [
    "# 在特征工程里读取的数据已经做了标准化\n",
    "# 线性回归\n",
    "#class sklearn.linear_model.LinearRegression(fit_intercept=True, normalize=False, copy_X=True, n_jobs=1)\n",
    "from sklearn.linear_model import LinearRegression\n",
    "\n",
    "# 使用默认配置初始化\n",
    "lr = LinearRegression()\n",
    "\n",
    "# 训练模型参数\n",
    "lr.fit(X_train, y_train)\n",
    "\n",
    "# 预测，下面计算score会自动调用predict\n",
    "lr_y_predict = lr.predict(X_test)\n",
    "lr_y_predict_train = lr.predict(X_train)\n",
    "\n",
    "#显示特征的回归系数\n",
    "print('LinearRegression coef',lr.coef_)\n",
    "\n",
    "print('LinearRegression intercept',lr.intercept_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The value of default measurement of LinearRegression on test is -7.12267893039e+20\n",
      "The value of default measurement of LinearRegression on train is 0.938930187969\n",
      "Mean squared error: 715862602398467489792.00\n"
     ]
    }
   ],
   "source": [
    "#验证集\n",
    "print('The value of default measurement of LinearRegression on test is', lr.score(X_test, y_test))\n",
    "\n",
    "#训练集\n",
    "print('The value of default measurement of LinearRegression on train is', lr.score(X_train, y_train))\n",
    "\n",
    "print(\"Mean squared error: %.2f\"\n",
    "      % mean_squared_error(lr_y_predict, y_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x1a1bd955c0>"
      ]
     },
     "execution_count": 98,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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1604FHm60SEmS9JI+97wz85eZOSkzp2XmNGqBfURm/ha4ATitOuv8KOCJzNwwsCVLkjSy\n9eZfxa4Bfga8ISLaImJRN6v/ELgPWAf8I/ChAalSkiRt1+OweWa+v4fl0+ruJ3BO42VJkqSu+K1i\nUgNaWwd2PUnqDS+PKklSYQxvSZIKY3hLklQYw1uSpMIY3pIkFcbwliSpMIa3JEmFMbwlSSqM4S1J\nUmEMb0mSCmN4S5JUGMNbkqTCGN6SJBXG8JYkqTCGtyRJhTG8JUkqjOEtSVJhDG9JkgpjeEuSVBjD\nW5KkwhjekiQVxvCWJKkwhrckSYUxvCVJKozhLUlSYQxvSZIKY3hLklQYw1uSpMIY3pIkFcbwliSp\nMIa3JEmF6TG8I+KbEfFIRNxVN++CiLg3Iu6MiO9HxIS6ZedGxLqI+FVEHDtYhUuSNFL1pud9OXBc\nh3k3AYdk5mHAr4FzASLiIOBk4ODqMd+IiFEDVq0kSeo5vDNzBfBYh3k3ZuaWavI2YGp1fwFwbWb+\nPjPvB9YBswewXkmSRryBOOZ9BvCj6v4U4KG6ZW3VvB1ExOKIWBkRK9vb2wegDEmSRoaGwjsiPgts\nAa7eNquT1bKzx2bmksxsycyW5ubmRsqQJGlEGd3fB0bEQuB4YF5mbgvoNmD/utWmAg/3vzxJktRR\nv3reEXEc8CnghMx8pm7RDcDJETEmIqYDM4CfN16mJEnapseed0RcA8wF9o6INuA8ameXjwFuigiA\n2zLzLzLz7oi4DriH2nD6OZm5dbCKlyRpJOoxvDPz/Z3Mvqyb9b8IfLGRoiRJUte8wpokSYUxvCVJ\nKozhLUlSYQxvSZIKY3hLklQYw1uSpMIY3pIkFcbwliSpMIa3JEmFMbwlSSqM4S1JUmEMb0mSCmN4\nS5JUGMNbkqTCGN6SJBXG8JYkqTCjh7oAqVGtrUNdgSTtXPa8JUkqjOEtSVJhDG9JkgpjeEuSVBjD\nW5Kkwni2ubQT9PaMeM+cl9Qb9rwlSSqM4S1JUmEMb0mSCmN4S5JUGMNbkqTCGN6SJBXG8JYkqTCG\ntyRJhekxvCPimxHxSETcVTdvYkTcFBFrq9s9q/kRERdFxLqIuDMijhjM4iVJGol60/O+HDiuw7xP\nA8szcwawvJoGeBcwo/pZDFwyMGVKkqRtegzvzFwBPNZh9gLgiur+FcCJdfOvzJrbgAkRMXmgipUk\nSf0/5r1PZm4AqG4nVfOnAA/VrddWzZMkSQNkoE9Yi07mZacrRiyOiJURsbK9vX2Ay5AkadfV3/De\nuG04vLp9pJrfBuxft95U4OHONpCZSzKzJTNbmpub+1mGJEkjT3+/EvQGYCHwper2+rr5H46Ia4G3\nAE9sG16X+sqvx5SkzvUY3hFxDTAX2Dsi2oDzqIX2dRGxCHgQeF+1+g+BdwPrgGeAPx+EmiVJGtF6\nDO/MfH8Xi+Z1sm4C5zRalCRJ6ppXWJMkqTCGtyRJhTG8JUkqjOEtSVJhDG9JkgpjeEuSVBjDW5Kk\nwhjekiQVxvCWJKkwhrckSYUxvCVJKozhLUlSYQxvSZIKY3hLklQYw1uSpMIY3pIkFcbwliSpMIa3\nJEmFMbwlSSqM4S1JUmEMb0mSCmN4S5JUGMNbkqTCGN6SJBXG8JYkqTCGtyRJhTG8JUkqjOEtSVJh\nDG9JkgpjeEuSVBjDW5KkwhjekiQVpqHwjoiPRsTdEXFXRFwTEWMjYnpE3B4RayNiaUS8YqCKlSRJ\nDYR3REwB/gpoycxDgFHAycCXga9m5gzgd8CigShUkiTVNDpsPhp4ZUSMBl4FbAD+CFhWLb8COLHB\nfUiSpDr9Du/MXA9cCDxILbSfAFYBj2fmlmq1NmBKZ4+PiMURsTIiVra3t/e3DEmSRpxGhs33BBYA\n04H9gN2Bd3Wyanb2+MxckpktmdnS3Nzc3zIkSRpxGhk2/2Pg/sxsz8wXgO8BfwBMqIbRAaYCDzdY\noyRJqtNIeD8IHBURr4qIAOYB9wA3A++t1lkIXN9YiZIkqV4jx7xvp3Zi2h3AL6ttLQE+BXwsItYB\newGXDUCdkiSpMrrnVbqWmecB53WYfR8wu5HtSpKkrnmFNUmSCmN4S5JUGMNbkqTCGN6SJBXG8JYk\nqTCGtyRJhTG8JUkqjOEtSVJhDG9JkgpjeEuSVBjDW5KkwhjekiQVxvCWJKkwhrckSYUxvCVJKozh\nLUlSYQxvSZIKY3hLklQYw1uSpMIY3pIkFcbwliSpMIa3JEmFMbwlSSqM4S1JUmEMb0mSCmN4S5JU\nGMNbkqTCjB7qAjSytLYOdQWSVD573pIkFcbwliSpMIa3JEmFMbwlSSpMQ+EdERMiYllE3BsRayJi\nTkRMjIibImJtdbvnQBUrSZIa73l/DfjfmflG4HBgDfBpYHlmzgCWV9OSJGmA9Du8I2IP4BjgMoDM\nfD4zHwcWAFdUq10BnNhokZIk6SWN9LwPANqBb0XEv0fEpRGxO7BPZm4AqG4ndfbgiFgcESsjYmV7\ne3sDZUiSNLI0Et6jgSOASzJzFvA0fRgiz8wlmdmSmS3Nzc0NlCFJ0sjSSHi3AW2ZeXs1vYxamG+M\niMkA1e0jjZUoSZLq9Tu8M/O3wEMR8YZq1jzgHuAGYGE1byFwfUMVSpKkl2n02uZ/CVwdEa8A7gP+\nnNoHgusiYhHwIPC+BvchSZLqNBTembkaaOlk0bxGtitJkrrmFdYkSSqM4S1JUmEMb0mSCmN4S5JU\nmEbPNpc0gFpbB3Y9Sbsme96SJBXG8JYkqTCGtyRJhTG8JUkqjOEtSVJhDG9JkgpjeEuSVBjDW5Kk\nwhjekiQVxvCWJKkwhrckSYUxvCVJKozhLUlSYQxvSZIKY3hLklQYw1uSpMIY3pIkFcbwliSpMIa3\nJEmFMbwlSSrM6KEuQFLftbYOzrqSymDPW5Kkwtjz1oCwdydJO489b0mSCmN4S5JUGMNbkqTCGN6S\nJBWm4fCOiFER8e8R8S/V9PSIuD0i1kbE0oh4ReNlSpKkbQai5/0RYE3d9JeBr2bmDOB3wKIB2Ick\nSao0FN4RMRWYD1xaTQfwR8CyapUrgBMb2YckSXq5Rnve/wP4JPBiNb0X8Hhmbqmm24ApnT0wIhZH\nxMqIWNne3t5gGZIkjRz9Du+IOB54JDNX1c/uZNXs7PGZuSQzWzKzpbm5ub9lSJI04jRyhbW3AidE\nxLuBscAe1HriEyJidNX7ngo83HiZkiRpm373vDPz3MycmpnTgJOBH2fmKcDNwHur1RYC1zdcpSRJ\n2m4w/s/7U8DHImIdtWPglw3CPiRJGrEG5ItJMvMW4Jbq/n3A7IHYriRJ2pFXWJMkqTCGtyRJhTG8\nJUkqjOEtSVJhDG9JkgpjeEuSVBjDW5KkwhjekiQVZkAu0qJdV2vrUFcgSerInrckSYUxvCVJKozh\nLUlSYQxvSZIKY3hLklQYw1uSpMIY3pIkFcbwliSpMIa3JEmFMbwlSSqM4S1JUmEMb0mSCmN4S5JU\nGMNbkqTCGN6SJBXG8JYkqTCGtyRJhTG8JUkqjOEtSVJhDG9JkgpjeEuSVBjDW5KkwhjekiQVpt/h\nHRH7R8TNEbEmIu6OiI9U8ydGxE0Rsba63XPgypUkSY30vLcA/yUz3wQcBZwTEQcBnwaWZ+YMYHk1\nLUmSBki/wzszN2TmHdX9p4A1wBRgAXBFtdoVwImNFilJkl4yeiA2EhHTgFnA7cA+mbkBagEfEZO6\neMxiYDHAa17zmoEoQ1InWlsHdj1JQ6/hE9YiYhzwv4C/zswne/u4zFySmS2Z2dLc3NxoGZIkjRgN\nhXdENFEL7qsz83vV7I0RMblaPhl4pLESJUlSvUbONg/gMmBNZn6lbtENwMLq/kLg+v6XJ0mSOmrk\nmPdbgVOBX0bE6mreZ4AvAddFxCLgQeB9jZUoSZLq9Tu8M/OnQHSxeF5/tytJkrrnFdYkSSqM4S1J\nUmEMb0mSCmN4S5JUGMNbkqTCGN6SJBVmQK5trvJ4HWtJKpc9b0mSCmN4S5JUGMNbkqTCGN6SJBXG\n8JYkqTCebb4L8Qxy7Qx9eZ/5npQGhz1vSZIKY3hLklQYw1uSpMJ4zFsS4PFpqST2vCVJKozhLUlS\nYQxvSZIKY3hLklQYT1gbBL098ccThCRJ/WHPW5KkwtjzLoA9dElSPXvekiQVxvCWJKkwhrckSYXx\nmPcQ8li2dnX+54U0OOx5S5JUmBHf87ZnIA09fw+lvrHnLUlSYXbZnref0CUNhcH42+PfM3U0aD3v\niDguIn4VEesi4tODtR9JkkaaQel5R8Qo4GLgHUAb8G8RcUNm3jMY+9sZ/OQrDb2h/D3clf4GDOU5\nBiWc31BCjYPV854NrMvM+zLzeeBaYMEg7UuSpBElMnPgNxrxXuC4zDyzmj4VeEtmfrhuncXA4mry\nDcCvern5vYFHB7DcXZ3t1Xe2Wd/YXn1je/XNSGuv12Zmc08rDdYJa9HJvJd9SsjMJcCSPm84YmVm\ntvS3sJHG9uo726xvbK++sb36xvbq3GANm7cB+9dNTwUeHqR9SZI0ogxWeP8bMCMipkfEK4CTgRsG\naV+SJI0ogzJsnplbIuLDwP8BRgHfzMy7B2jzfR5qH+Fsr76zzfrG9uob26tvbK9ODMoJa5IkafB4\neVRJkgpjeEuSVJhhH94R8b6IuDsiXoyILv9dwMux1kTExIi4KSLWVrd7drHe1ohYXf2MuJMJe3q/\nRMSYiFhaLb89Iqbt/CqHj1601+kR0V73njpzKOocLiLimxHxSETc1cXyiIiLqva8MyKO2Nk1Die9\naK+5EfFE3fvr8zu7xuFm2Ic3cBfwn4AVXa1QdznWdwEHAe+PiIN2TnnDzqeB5Zk5A1heTXfm2cyc\nWf2csPPKG3q9fL8sAn6Xma8Hvgp8eedWOXz04fdrad176tKdWuTwczlwXDfL3wXMqH4WA5fshJqG\ns8vpvr0AflL3/jp/J9Q0rA378M7MNZnZ09XXvBzrSxYAV1T3rwBOHMJahqvevF/q23EZMC8iOrv4\n0Ejg71cfZeYK4LFuVlkAXJk1twETImLyzqlu+OlFe6mDYR/evTQFeKhuuq2aNxLtk5kbAKrbSV2s\nNzYiVkbEbREx0gK+N++X7etk5hbgCWCvnVLd8NPb368/rYaAl0XE/p0s10v8m9V3cyLiFxHxo4g4\neKiLGWrD4vu8I+L/Avt2suizmXl9bzbRybxd9n/gumuvPmzmNZn5cEQcAPw4In6Zmf8xMBUOe715\nv4yo91QPetMW/wxck5m/j4i/oDZq8UeDXlm5fH/1zR3Urvm9OSLeDfwTtUMOI9awCO/M/OMGNzGi\nLsfaXXtFxMaImJyZG6phuEe62MbD1e19EXELMAsYKeHdm/fLtnXaImI0MJ6RO6zXY3tl5qa6yX9k\nBJ8j0Esj6m9WozLzybr7P4yIb0TE3pk5kr6w5GV2lWFzL8f6khuAhdX9hcAOIxcRsWdEjKnu7w28\nFSj2u9b7oTfvl/p2fC/w4xy5VzTqsb06HK89AVizE+sr0Q3AadVZ50cBT2w73KUdRcS+2845iYjZ\n1LJrU/eP2rUNi553dyLiT4C/B5qBH0TE6sw8NiL2Ay7NzHcP8uVYS/Ml4LqIWAQ8CLwPoPo3u7+o\nvqb1TcA/RMSL1H4JvpSZIya8u3q/RMT5wMrMvAG4DPh2RKyj1uM+eegqHlq9bK+/iogTgC3U2uv0\nISt4GIiIa4C5wN4R0QacBzQBZOb/BH4IvBtYBzwD/PnQVDo89KK93gucHRFbgGeBk0fwh2nAy6NK\nklScXWXYXJKkEcPwliSpMIa3JEmFMbwlSSqM4S1JUmEMb0mSCmN4S5JUmP8PVbWwCcX/U68AAAAA\nSUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a228bf278>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#在训练集上观察预测残差的分布，看是否符合模型假设：噪声为0均值的高斯噪声\n",
    "f, ax = plt.subplots(figsize=(7, 5)) \n",
    "f.tight_layout() \n",
    "ax.hist(y_train - lr_y_predict_train,bins=40, label='Residuals Linear', color='b', alpha=.5); \n",
    "ax.set_title(\"Histogram of Residuals\") \n",
    "ax.legend(loc='best')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x1a1e44a358>]"
      ]
     },
     "execution_count": 99,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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o0QOfffYZrrzySsTExAS7WxQBuCubiMgLSiksWbKkIZABYNSoUQxl0gyDmYjIQ+fOnUNO\nTg5mzJiBQYMGITMzM9hdogjEYCYi8sCOHTuQlZWFlStX4plnnkFubi46d+4c7G5RBOIaMxGRBwoK\nCmCxWLB582YMGTLEo9+JpBuPqOVwVzYRtUqehObJkyexd+9ejBo1CkoplJWVITEx0eP2Q/3GI2o5\nvF2KiMgFTy5Q2LBhAzIzM3HHHXegoqICIuJxKAORd+MRtRwGMxG1Oq5Cs6amBo888gjGjBmDtLQ0\nbNy4EXFxcV6/RqTdeEQth2vMRNTqOAvH46dLcOWVV2LHjh2499578cILL/gUyoC2taCpdeGImYha\nHWfh2CUtGddccw3ef/99vP766z6HMhB5Nx5Ry+GImYhaHfsLFKw1VSje+HekZl+PuZNuxoSsEZq8\nBmtBk68YzETU6tjCccGSddj37h9RW2zC5DFDNQ/NSLrxiFoOg5mIWh2lFI5v+Qjf/X9zkNauHd75\n4guMGKHNSJnIXwxmImp13n33XTzwwAMYN24cli5dirS0NKfPskgItTQGMxGFFX+Csry8HPHx8Zg8\neTJEBHfccQdExOVr2RcJsZ13BsBwpoDhrmwiChueFAZxpLa2FgsWLMAvfvELFBUVISoqCjk5OS5D\nGWCREAoOjpiJKGy4CkpnI9hjx44hJycH//rXvzBt2jS31zPaj8idFSxmkRAKJAYzEYUNT6tp2cL1\n+52bcPazlxElVixbtgxTp0512b6j+taOsEgIBRKDmYjChifVtGzhWlFTi9LC9dAldUDnW+Yh4ZLh\nbtt3NCJvylGREG4QIy1xjZmIwoYn1bSeXLYBpUU/QkSQev0cdJz6PCyJHT1aF3Y1RS0A0pONzW6H\n8nXdm8gZjpiJKGy4qqallMI///lPFL76G8R2H4D2t/4eutiEht/1ZF3Y2Yg8PdmILfMcn3P2Zd2b\nyBUGMxEFlNbTvI6qaZ07dw733nsvVqxYgaQLs5Aw+v5mv+fJurB9qU4bd/WteYsUac3vqWwRiRWR\nHSJSKCL7RORJLTpGROGvJaZ59+/fj6ysLHzwwQd45pln8Oby1Uhs277RM55eHjEhKx3P3dIP6clG\np1PXTTkLfG4QI19pMWKuBjBCKVUmIgYA/xaRz5RS2zRom4jCWEtM86anp+PCCy/EO++8gyFDhgAA\n9Hq9z6N0b+tb+zLKJnLF72BWSikAZfVfGur/cnb8j4hakUBN8548eRJPPfUUFi9ejMTEROTm5jb6\neUteHsFbpEhrmqwxi4geQD6AXgD+qpTarkW7RBTePDne5K3169dj2rRpKDl3DpstF6E8pbfTMGyp\nY0y8RYq0pMlxKaWURSk1AEAXAINEpG/TZ0RkpojkiUje6dOntXhZIgpxjo43GfSC8upa9Jy3DkMX\nbvR4vbmmpgZz587Fddddh5jEFKT/8mWUpfR2unbNY0wUrjQ9x6yUKgHwFYDrHPzsDaVUtlIq29VN\nLkQUOZpupkqJMwAKKKk0ex2W999/P/785z/jvvvuQ+fpL8Ka3KXRz5vWsGadawpXWuzKThOR5Po/\nGwGMArDf33aJKDJMyErHlnkjcGjheMRFR8FsbbwFxV1Yms1mAMCjjz6KDz74AK+99hpOlVsdPmu/\ndt1Sx5hW7zJh6MKNXs8AEDmjxRpzJwBv1a8z6wCsVEqt1aBdIoow3oRleXk5brzjbhT+8CMSxs1F\nekoc5o4ZDMCztetArG83xWshKRD8HjErpXYrpbKUUv2VUn2VUk9p0TEiijyenvktKChARt9MbFyz\nEubETlDK2mja25PSnJ484y9Ol1MgsFY2EbUYd2GplMIrr7yCyy+/HD+dKUH7yU8j5ao7Ibq637E/\nA+2uEIgvxUK8xapfFAgsyUlELcbdmd8zZ87gqaeewujRo1HYcwp0cUnN2rCFnidHlAJ9jKklpsup\n9WEwE1GLchSW//nPf5CZmYnU1FRs374dPXv2xLA/bQr50GPVLwoETmUTUdDU1tZiwYIFGDhwIF5/\n/XUAwAUXXAARaZE1Yn+1xHQ5tT4cMRNRUBw7dgw5OTn417/+hWnTpmHatGmNfh4upS5Z9Yu0xmAm\nohb32WefIScnB2azGcuWLcPUqVMdPsfQo9aIwUxELa5Nmza46KKLsGzZMvTu3duvtlqqHjZRS+Ea\nMxG1iO+++w4vv/wyAGDo0KHYunWrJqHMetgUaThiJqKAUkrhzTffxG9/+1skJiaibeZIvL7tJ49G\nuO5Gwy1x3zNRS2MwE1HAnDt3DjNnzsTKlSsxcuRI3PHIIjz75XGHJSyBxhu9hvdJw6p8k8tylyzw\nQZGIwUxEfnE2qq2trcWQIUNw4MABPPvss3j00Udx5aKvHI5wF6zZh+paa6MQfnfbUagmr9V0NMwC\nHxSJRKmm/+oHXnZ2tsrLy2vx1yWixmyhaiqphF4EFqWQ7sUGqqaXOAAAlAJEkJ5sRPvTO3GwIg6l\nSRc4DVFvCYBDC8c7fX2jQc+zxBRyRCRfKZXtybMcMRO1Uk1DzVL/Id1UUom57xcCcH9DUtM1XktZ\nMYrWvYD4viNgumQ4TIa+QH1VTVNJJQRoNgr2lv1oOFzOOhN5g8FM1Eo52jhlY7YqLFizz23A2a/l\nVv6Qj6J1L0LVVCL+4qsdPq+AZuFsNOgRa9ChuMLc7HlHzzat/MWzzhRpGMxEYc7Xc7zuNkiVVDYP\nyqY6Jxtx/Mx5lGxehvM7PoQhtTtSpzyL6NRuTn9Hoa50pX1/ATickr71snRs2n+ao2FqVRjMRGGs\n6XS0o53Lzmix5jt3TAYeeH4pzu/4EAlZ45Ay/G7oDDEufyc92Ygt80Y4/BmnpIm4+YsorA1duNFh\nuLoKPxuHG7cctOMsIP/3v/+hd+/eWL3LhPlL1uJcXBe3a8jcmEWtlTebv1j5iyiM+XOO13YzUrLR\n4PQZR5W0ysrKMGPGDPTt2xf79tWtQxf+5V4cXjgeL04a0OimpamDu/HmJSIvcSqbKIw5m47WiaDn\nvHVup4RtG6fsj001ZX92uKCgAJMnT8Z///tfPPHEE8jIcH0FY3b3tnh6Qr+Gr1fvMmHowo0Op6tZ\n85qoDqeyicKYJ9PR3kwf95y3zuFUtAB4uOthPPTQQ0hISkGnm+aivF2fRgHq7kyxq58Djjd/cYRN\nkYLnmIlaiabneHX1RULsOasd7WiE6qqS1okTJ9D/8qtQMvAelBkSADTebOaubrWrn9v+7Em/iSId\ng5kozNmf4+0xb53DZ5qGrbPd3Ldelt6oPnXV0d2IiYrC3El34Ib+V2PToq9gPlfVqC1bgLpb7/Zl\nPZw1r6k1YjATaSjY66R6ByNm2/ft++ZsZL1p/2k8d0s/LPr0W3z76T9xbusK9L3sCkzImgsA+LFJ\nKNucKKlEktHg8OyzrVKXu7rWrHlNVIe7sok0Egp3AzsKZdv37fvm7DlTSSUubWeB/vOncO6b5fjl\n9OnYuml9w8+dBWWS0YDymtpm3zfopKGAyNwxGTAa9I1+bqvk5exnw/ukYejCjeg5bx2GLtzIe5ap\nVeCImUgjztZQ56z0rO60FtKdjEr1Ii43iNlYi09gwIA7UVtbi3feeQc5OTmNfj53TIbDTVoigNnS\nPOwTYqMa3rcnda29vfaRKBJxVzaRRpztaAZabofx6l0mzH2/EGbrzz0x6KTR164opTBJbcbMmTPR\nq1cvp6/RNFxnryhw+t6blt/09O+BP8VTiEINd2UTBYGrEpctusNYmn+dEmdweEkEANQUHcXZDX9D\n6viH0L17dwwZMwvTPziAEyUHHIapo0sjnJ2BFvy8duztiNef4ilE4YxrzEQacbROas9ZoNiKbmix\njvr8+gPNppTNFgWl0KxvSimUFq7Hybdmw3zmGPQVZzC8T5pP6+SO3ruj8pz2x6PccbaezQ1hFOn8\nDmYR6Soim0TkOxHZJyKztOgYUbixlbjUS9Mha53kuOalL7XeMOYs/M9VmvHcLf0aymN2iLEgfsur\nOPv5K4hJ74NLZ/0dLz84BZv2n3Z51tgZ23u3L7/pbGrb0xGvq81iRJFMi6nsWgBzlFL/EZFEAPki\nkquU+laDtonCim2Kdu4Hhc1GrmVVtVi9y9Rss5OWhTWSnUxZd042Niq/+ZtZs3Him1x0G3MPXnzm\nD7jlsq4AgNkrChy262ntbfs+O1sj9nTE68lmMaJI5HcwK6V+BPBj/Z9LReQ7AOkAGMzUKk3ISseC\nNfuanek1W1WzwPV3HdV+I1ZynAHnHISyQV93ZMlqteLtjbvxp69PQn/ZRHTsMRjSOQOPr94HnU6H\nCVnpbs8ae8PZDm5vRryO1rOJIp2ma8wi0gNAFoDtWrZLFC5s68WOCm0AzQPXn3XUptPgxRVmWB08\nFx8dhcGd9BgzZgx+M/UWVFRWQRcTh5jOdQFpP1Wt5fSxo+lt1r4mck+zXdkikgBgFYAHlVLnHfx8\nJoCZANCtWzetXpYoZHhyoUTTwPV1VLl6lwlzVhY6LRRi78d929C//1SUlZUh7up7AH3z/+xtHxi0\nnj7miJfIe5oEs4gYUBfK7yqlPnT0jFLqDQBvAHXnmLV4XaJQ4mi92J6jwPUlCG0fANyFsrKYUbJ5\nGc7v+BDdemXgq6++wh3vH3c4mk+yu5PZ1zANdjlSokjhdzCLiAB4E8B3SqkX/O8SUXhytS6c7iKo\nvA1Cdx8AGiig6kghErLGI3b0Pbj44oshctzho042knvM2aUYAKt0EXlLixHzUAB3AtgjIrYtnb9T\nSn2qQdtEYcPZxil/KlU9sXoP3tt+DBaloBfBlMu7ut0YVr7/3zD2GABdbAI65PwJOkMsqut/VuKk\nyIiz73tK693lRK2ZFruy/43mtYaIWh0tdiHby/n7Vmw5eLbha4tSeGfbUcRH61Fe03zEbK2pxNnc\n11G+90skDZ2C5GE50BliGz3jz65rV1PVrNJFpB1W/iLSiJa7kFfvMjUKZXsVNRYYdI0/C9ecOogf\n33oQ5fs2IWnIFCQNmdzo57bHfd117a4QCqt0EWmHtbKJPODpxiZn68XeboxyVWlLAY3mqCr++w1O\nr1kEvTEJHSY/g9hu/Zr9zhUXtG3on619R31x1k93U9VazxYQtWYMZiI3PNnY5Cp4fdkY5W4K2L6q\nWHSnDMT/4iqkDL8b+rgkh88fPvNze64+PDjrp7upalbpItIOg5nIDXejRVeBBsDheWN3G6Nc3VQF\nAFVHd6Nsdy7ajZ+NqMR2SB3/kMv34Mlar6v36cnaNI9ZEWmDa8xEbrgbLToLtAVr9rk8b+wqLJ3d\nVHVFjzao2vr/49R7j6P6x//CUlbs0XvwZK3X1fsM1IUSWl/iQRQJOGImcsPdaNFZoDkry9n09x1x\nNDX8y/4JeOvZh3BqyxbE9x2FttfeC120+8A1GvQY3icNWU9taLjgItlowIIbL2k0MnX1PgM1Vc1j\nVkTNMZiJ3HC3scndtLMjAmB4nzSn07hNv//w6Isw547rcPTwIaTe8DDiL77GZft6EViVQudkI4b3\nScOKnccarUuXVJox9/1CAHY3Yrl5n75MVbubpuYxK6LmRHlQa1dr2dnZKi8vr8Vfl8hX3mzuAuoC\nLdagc3gFo41BJ4A03shlNOhx62XpDUFqNVdDdDqI3oDqH/8HXWwCDCmdXPbVaNA3Oqbl7PpFoHnx\nEy3Xe539ffGkb/4UZSEKRSKSr5TK9uhZBjO1VlqHkP31i0rVjUoF9cebfFBTdBRFaxbB2CMLKSPu\n9uh3HE1R95y3zmkfBMChheN97KFrnoSuJ+FNFAm8CWZu/qJWSetNRxOy0rFl3gi8OGkAqszWhvVl\nBe/L4imlUFrwOU6+NRuW8mLEds/0+HdFmh/BcrWWHcgCIJ5MU0/ISsetl6VDX1+sWy+CWy/jjVTU\nujGYqVVytenIHwvW7GvWrgIagscda1UZij7+E86ufxUx6b9Ap7tegfFCjz5kA6i7k7nph4u5YzKg\n1zl+/eF90jxu21ueVANbvcuEVfmmhp3rFqWwKt/EXdnUqjGYqVUKxKaj1btMTndiW5TyaORcW1qE\nykP5SL76l2g/6SlEJbT1uh9NP1xMyEpHYozjfZ6b9p/2un1PeXLEKlAfkIjCGXdlU6vk7WUOnqxH\nuwoTV2vNSllReXAn4npdjui0Hki/75/QGxM9fSvNOHpf55x8YDCVVKLnvHUBKezhyREr7somao7B\nTK2SN7WdHVX2mvt+IZ78ZB/2neNwAAAgAElEQVRKKswNgeMqTJyFcm3ZWZxZuxhVRwrR4Y6FSOze\nD4aENo12atukJxtRUVPrcqc3UPchYPUuk0dnlG19C9T9ye6OWPlz2xVRpOKubGq1mo6Ch/dJw6b9\np5t97e0ZZU9VHsxD0acvQtVUIWXUvUjofy1EBMlGA0TQKPRt4eZqh7U9R8egmn4Q8eT3Ao27sqm1\n8GZXNkfMRAAqamqxYscxmK11sWcqqcQ724763a7RoEdMlK7Z2nPJv9/FuS3vwZDWA2lTHoUhtevP\nP6s0w2jQ48VJA5qFU3Kcwe2IGWg+Fdx0WtlZuLf0FDIvvyBqjsFMrVLTkZonYeet9PqQAdBsVGhI\n7Y7ES8cjZfjdkKjoZr/rrCylpxNcyXGGZt+zn1Z2dsY4GFPIvl5+QRSpuCubWiVHu4G1Zhv5TchK\nx3O39IP672aUFnwGAIjvMwxtr/21w1C2cTR6dbaJqyl3AR6oSymIyH8MZmqVWmLK9rEP9+CJ1Xsw\n+Ml1mDptGo5+tAhJJ/PRuU0MBHUj6pcmDUC6k1GqQt3I1v5Mr6cj2nOVdeeZhy7ciJ7z1jVrx/Zh\nIT3Z2NAXrusShQZu/qKw5U9JTVf1o7VUffJ7FK1ZhNqSk0i6YhI6XpODhbc1Xjt2tzHLfjOUp5u4\nUuIMqDJbuamKKESwJCdFPG9LajYdPQ7vk+bwvmMt1Zaewal3H4EyV6PD5GeQfGUOqiyOC4DYRq+O\n2BfcsD2bbGy+hmwjqJvKZuEOovDEYKaw5Kxi1JyVhc3C2VGIr8o34dbL0humcp1UrPSJqq1bB45K\nbId2Y3+LTnf9BbHd+jX83NFI3VZr21k3bIVAhi7ciLwjZ1Fda3X4nADIGdzN6Vo0C3cQhT4GM4Ul\nZwFjUarZyNlR/epKswWb9p9uuHjCqtGKTtWR3TC9MRNVR3cDAOIvvgb6uKRGz9gKgDjiag3Z9qHi\n3W1HHU5l60Xw4qQBeHpCP4/qVBNRaGIwU1hyFTCVZgseXFGAoQs34onVe5zWrzaVVDasU/sqJc5Q\nNwVttaDkX8twavnjEEM0dDEJTn9HwXn5Tke7pR39viNWpRrWj7nrmih8cfMXhSVPN0G5uw/ZoBeH\n5S89JQC+vr8f7rjjDmzZsgXx/Uah7ah7oYt2PTK1vwfZVQUyb3uWbrcJTsv7ponIP6z8RRHPFjBz\nVhY2XBnoiLtgM1uU2/B2JTnOgI8++gi7d+9Gt1vnQXoN8/j3AMd1uFflmxp2TzvbPe6sz01rXjOI\nicIPp7IpbE3ISsfi2zP93l2tABia7P4yGvSYOrgbUhxU0AIAq7ka1Se/R7XZghXllyBp6ivQ9fYs\nlIG6SmMDntyAJz9xvP5tm+p2NiWdM7ibR7u4iSj8cMRMYafpFO2tl6X7fdlEQmwU4qKjGk0nr8o3\nOZwqrzl9BEVrFsFSdhaG+95EhTkO+japHpfLtHG29g38vLnNXS1pZ5dacPc1UfjSJJhF5J8Argfw\nk1KqrxZtEjniauoXAGavKPBpWrqkwoz5N1zSEIDvbT/WbIpcKYWywvUo/vLvkGgjUm94GLqYOH/f\nkkP2m9tcTUnz2kSiyKPVVPZSANdp1BaRU87OL9sufPB1rTjJaGh01rlZKFvMKPp4Ic6ufxUxXS5G\n57tegfGCy3x8Nde82T3N3ddEkUeTEbNSarOI9NCiLSJXnE3RmkoqMXThRp/bFWleKavRz/UGSLQR\nydf8Em0G3QIR7bZnJBsNiI+J8mn3NK9NJIo8XGOmsOJs6lbguKKWJwR1U9lNKWXF+e0fwthrEKJT\nu6Hd2FkQ8a1EmG0TWdPrJY0GPRbceIlfQcrd10SRpcV2ZYvITBHJE5G806dPt9TLUoRxNHXrz3En\noG603PT+4trSM/hpxe9R8vVSlH/7Vf1zvtftnH/DJdj1h9ENt0nxRicickazAiP1U9lrPdn8xQIj\n5A/brmxTSSX0Ii7PMXvDFvCVB3eiaN2LUOZqpIy6Fwn9r/UrlKcO7oanJ/Rz/yARRSwWGKGIZhth\nelL5yxsKQMX/tuH0h0/DkNYDaTc+CkNqV7/bZSgTkTc0mcoWkfcAbAWQISLHReRuLdolcsbR7mx/\nKGtdW8aelyH56unoNO0FTUJZL9JwK5SziyuIiOxptSt7ihbtEHlKywIaZfs24fz2Veh4x0LoYhOQ\nNHiiZm3bptmblsokInKGU9kUEuyreSUZDRCp2ynd2cmlDDoN1patNZU4m/sayvduREyXi2GtrdFs\nN6SjtW/789ZERM4wmCnomlbzsi9VaRtp5h0526hEpr+hXH3yexStWYTakpNIGjIFSUMnQ3T+1dy2\nSU82Oh3R+zvS541RRJGPwUya8zY83K0XV5oteGfbUU37WLL5bShzNTpMfgax3bzfnKUTwKqaH9Wy\nVd2y7Rpvyp9SmY7KkXJ6nCjyMJhJU76ER0tduGCpOAcoK/TxKUgdNxvQ66E3tvGqjfQmHzRcfQhp\numvc31KZ7sqRElFkYDCTpnwJD2fVvLRUeaQQZ9YuRnSHC9H+tvnQJ6T41I6j+44dva9AlMoM1PQ4\nEYUWBjNpypfwmDsmQ/MzyTbKakHJv9/F+a3vI6ptOpKvutPvNj0dpWpdKpM3SRG1Dgxm0oRtStfZ\nlixH4WE/DRxr0L46bG1pEYo+/hOqTd8hof9opIycCV10rCZtB2OU6ugDDG+SIoo8DGbyW9N1ZUd6\ntGsczE1/p9Js1bxfYoiFtboCqTfMRfzFV2vadjBGqbxJiqh1YDCT3zypwvXNwbNYvcvUKFwCMXVt\nNVejNO9jtBl0M/SxCeh01180OwZlE8xRKm+SIop8DGbymyfTugpotC4biKngmtNHULTmTzAXHYWh\nfU/EXTjQ61A26AQJsVHNrme0se3KBoChCzdy5EpEmmMwk9883VVtKqlsCDMtKnfZKKVQVvg5ir/8\nOyQ6Du1vfwrGnpd63Y4AMFvr+mTQScOfbT/Lqb8liueJiSiQWuw+Zopcju5IdsZUUgkF/yt32Sv5\neinOrv8rYrpcgs4zXvEplIGfC4UUV5jRdMVbAViVb2rYsObsSBgRkb84Yia/2a8bB/o8sj2lFEQE\n8ZcMh87YBm0G3QwRbT5rWqzNPzjYwpfniYkokDhiJk1MyErHlnkjkN4Cu5WV1YJzW1fi7PpXAQDR\naT2QdPmtmoWyK7Y1ZUd4npiItMBgJk15M63ti9rSM/hp5e9RsvltWKsroCy1frWnF/HqedtGr6bv\nkeeJiUgrnMomTU3ISkfekbN4b/sxTdeRAaDi4E6cWfciVG012o39LeL7XQvxMljtCVyvdeuARmvN\ntvDleWIiCiQGM2lq9S4TVuWbNA9la1UZznzyZ+jbpCHtxkdhSO3qd5vueqjXC9pER+FcpblZ+PI8\nMREFCoOZfGJfTjM5zgClgHOVZk2PQQFA7fnT0CemQhebgPaTnkZ0WndIVLRm7btitijEx0ShYP7o\nFnk9IiKAwUxesIWxqaSy0T3E9sU4tAzlsr0bcTb3NaRcPR2Jl16PmE69NWnXaNAjJkqHkkrHRUTs\ncac1EbU0BjN5ZPUuE+a+X9hQdEPbierGrNUVOJv7Gsr3bUJM174w9hqsWdspcQbMv+ESAM3vS3aE\nO62JqKUxmMkjC9bsa1QJK1CqT36PojV/Qm3JKSQNvQNJQyZpVutarxPMv+GSRmvDtun4JKMB5TW1\nMFt+fo/caU1EwcBgpkbs147tNzx5Mu2rBWtVGZSlFh2mPIvYrn09+h0BMOTCtthy8KzL5yxWhTkr\nCwH8vHnLPqSdvXciopYkSuPds57Izs5WeXl5Lf66rY23QePo+kajQY/nbumHB1cUBKyflopzqDpc\n0HA1o6o1Q6IMXrWhF8HgC1LwzQ9n4e5fadt7YugSUUsRkXylVLYnz7LASISyhaytNrXtooXVu0xO\nf8dVDej46MAUDak8UogflzyAM5//BZbyYgDwOpSBuk1nOw4VI9logMB14RDWtSaiUMZgjlC+XLTg\nbAeyqaQS5TXa3p2sLLUo3vw2flr+BCQ6Dh2nPg99fIpfbZqtCsUVZo8uyeBuayIKVQzmCOUqZJ2N\nmltqB7JSVpxa8QTOb12JhP7XotP0lxDd/oIWeW0b7rYmolDFYI5QroLH2ZR2oOtc24joEN/nSqTe\nMBftxv4WuujYgL4e61oTUThhMEcoVyHrbEp7QlY6nrulX0BuiLKaq3Bm/auo+O83AIDES8c3bPYK\npPRkY8N7EruvufGLiEKVJselROQ6AC8D0AP4h1JqoRbtku9sweNsN7WzqW7b781eUaBZEZGa04dR\ntGYRzEVHoU9MRdxFQzRq+WfJLs4hs641EYUTv4NZRPQA/grgWgDHAewUkTVKqW/9bZv8MyErvaGE\nZlOuprqfX39Ak1BWSqGs4DMUb/wHJDoO7W9/Csael2rQcmOHF44HwHPIRBQZtBgxDwLwvVLqBwAQ\nkeUAbgLAYA4Bc8dkODybPLxPGoYu3OgwxBwFuS+qjhTi7Ia/IbZHFlKvf8jvXdeO2E+7c2RMRJFA\ni2BOB3DM7uvjAC7XoF3SgKO7g4f3ScOqfFNDWNvOONuet7+gwheWylLojYmI7Z6JtFt/D+OFAyGi\n/XYGATC8T5rm7RIRBZPflb9EZCKAMUqpe+q/vhPAIKXUA02emwlgJgB069btsiNHjvj1uuS7oQs3\nOhwV6/28slFZLTi37X2c374KHe9cjOjUbv500yOs4kVE4cCbyl9ajJiPA7C/tb4LgBNNH1JKvQHg\nDaCuJKcGrxvRtF4vtW/P2d98f0K5trQIRWtfQPXR3Yj7xdWISkz1uS1nHI3kbTvMGcxEFCm0COad\nAHqLSE8AJgCTAdyhQbutVtOa1U2nmv1tT2sV3+/AmU9fgqqtRrtxDyK+70iIi5KYziQbDU4vy3A1\nmmcVLyKKJH4v/CmlagH8H4D1AL4DsFIptc/fdlszX8ppetuelqoOF0Cf2A6dpr+EhH6jfAplAIiP\nicLUwd0cFgRZfHum0/PVrOJFRJFEk3PMSqlPAXyqRVvkfATo68hQq13W9sxnTbDWVCKmYy+kXHMX\nAAWJivarTVNJJVblm3DrZenYtP+0w2l8RzvMWcWLiCIJ72MOQZ2TjW7PHnu6Bu3qNim9CDomxXod\n3GV7v8TZDa/B0K4rOk57wafboJypNFuwaf9pbJk3otnPHO0w51llIoo0DOYQ5OzssW1k6OkatO05\nZyxKeRXK1uoKnM19DeX7NiGma1+kXv+wz9PWrriaGeBZZSKKdAzmEORuZOhsDXrOykLMXlHQ8LyW\na8u154twavljqC05haRhOUi64naILjAXXnDNmIhaMwZziHI1MnQ2orTtWjaVVDqtke0rfUIKYjr3\nQbuxsxDbta8mbSYbDaiutXLNmIjIDoNZYy1Rr9nZGrTWLBXnULzxH0i5Zgb0CSlIvX6OZm0bDXos\nuPESAFwzJiKyx2DWkNbnj51xtAattcojhTizdjEslaWIyxiGuN7aVVlNbxLADGIiop8xmDXk6vyx\nluHTdA1a52cpTXvKUouSf7+L89s+QFTbdHSauADR7S/QpO2XJg1gCBMRucFg1pDW54+bsk2Tm0oq\nGyphpcQZUG22oMKsTTCXbHkP57e9j4T+o5EyciZ00bF+t6kT4IXbGcpERJ5gMGvIk/PHvmo6TW4b\nIRdXOC5h6S2ruQo6QyzaDJyA6A4XID5jqCbtChjKRETeYDBryN35Y2803URWXl0bkDVlq7kKxV/+\nHTU//YCOOYugNyZqFsp6nWDxxEyGMhGRFxjMGtKqMpWjTWSBUHP6MIo+XgTzmaNoc/ltqBvfaoeh\nTETkPb/vY/ZFdna2ysvLa/HXbUn+HJtydl+yVpRSKCv4DMUb/wGJiUPq+Dkw9szS9DWSjQYUzB+t\naZtEROGqpe9jpib8PTYV8GsMLWaU5q+tK6s5fjb08SmaNm/QScMZZSIi8o7f1z5Sc/5e2+hss1hK\nnH+XRVSfOABrTSUkKhodpjyD9hMXaB7KehE8zylsIiKfMZgDwN9jU8P7pDn8/sWdEn3qj7JaUPLN\ncpx8Zy7OfbMCAKCPT4GItv/4bfcmM5SJiHzHqewA8OfY1OpdJry3/ZjDn205eNbrvtSWFqFo7WJU\nH92DuIuvRtIVt3vdhiu27WIsp0lEpA0Gswu+buBydWzKVZu2tWmtqnhVHduL0x89C1VbjXbjHkR8\n35EBuabx0MLxmrdJRNRaMZid8GcDl7NjUwBctqnlNY0AoE9MhSGtO9qN/g0M7bpo1q49XtFIRKQt\nBrMT/ta9dnRt49CFG122qcVubPNZE8p2b0Dy1b+EIbkjOk55zu82neEVjURE2mMwOxGIutfu2kyO\nM/hVYrNs75c4u+E1iN6AhAFjYUju6HNb7jS9IYqIiLTBYHYiEHWv3bXp69KytboCZ3NfQ/m+TXVn\nk294GFGJqT730x0BsGXeiIC1T0TUmjGYnfC17rWjzV0AGm6FEgD2+Ws06DG8TxqGLtyIkkrvR8tK\nKfz0wQJUm/YjaVgOkq64HaLTe92ON7iuTEQUOAxmJ3ype+1ow9jc9wthUQrW+jS2D2W9CC7tloQV\nO4/BbPFuuKyUFVAKotMj+cqpgOgQ27WvV224YtAJJg3qilX5Jk0u5SAiIs8wmF1wtIHLFUcbxsxW\n54FrUcqns8mW8hIUrXsRMZ0vQvKwHMR26+91G+48PzETALC28MeG95QSZ8D8Gy7hujIRUQAxmDUU\nyIsnbCoPF+DM2sWwVJUhrvflAXmN9Pqp6qZT+VVma0Bej4iIfsaSnBpZvcuk8aWJjSlLLYq/Xoqf\nVvweutgEdJr2AhKzxmn+Orapan/rfRMRkW8YzBp5fv0BBPICTfOZYzi/YzUSMkej47QXEd2+p2Zt\nS/1f6clGPHdLP5dnqgN+8xURUSvHqWyNBCqwak4dRHSHCxHdvic63/1XGNpqu75r0Dm+DSoQx8WI\niMg9v0bMIjJRRPaJiFVEPLoAOlJ5GlgCwGjQuZ32tpqrcObzV/Dj0lmoPFwAAJqHcrLR4PSKxrlj\nMmA0ND52xR3ZRESB5+9U9l4AtwDYrEFfwpqjIHOkbrpb8OKkAU6fqTl9GCffeghlhRvQZvBtmhyD\nsv8gkGw04KVJA1Awf7TTHdYTstLx3C39kJ5sbDbNTUREgePXVLZS6jsAAbmxKNw0Pffsar250mzB\nk5/sc/iz0sINKP7idehi4tF+0h9h7OE8wD2VbDSgYP5or3/P2+NiRETkP64xa8g+nN0dnSquMCPO\noENFkyNIotMhpms/pI6fDX18sib98qWiGBERBYfbYBaRLwA4ug3hcaXUx56+kIjMBDATALp16+Zx\nB8NJ08pf7tjOBVcd/xaW0iLE/+IqxPcdGbB7k4mIKPS5DWal1CgtXkgp9QaANwAgOzs7kCeLgmL1\nLhPmrKwrv+kpi9WCc1tX4tyW92BI7Ya4jKEBqXOdEmfQvE0iIgoMTmVrwDZS9iaUa0uLULR2MaqP\n7kHcxVej3ejfBCSUDXrB/Bsu0bxdIiIKDL+CWURuBvAKgDQA60SkQCk1RpOehZEFa/Z5PH0NAJaK\nc/hxyW+hamvQbtxsxPcdEZCpa70Inr/N8XEoIiIKTf7uyv4IwEca9SUkObrG0T7oVu8yeby5SikF\nEYE+LglJV9wO4wXZMLTr4nPf9CKYcnlXrNv9I4orGvfBaNDzeBMRURhiSU4XbFPUpvrjT6aSSjz2\n4R6s3mVqeMbT2tHmM8dxctlDqD5R93ybgRP8CmUAmHJ5V2R3b4u46LrPV/r6UTfPHBMRhS+uMbvg\n7CKHOSsLkXfkLNYW/uh2tKyUQvneL3E293VIVDSs1eWa9W9t4Y+N7ku2KNVQnYuhTEQUnhjMLjir\nf21RCu9sO+r2963VFTiz4a+o+PZrxHTrh9Tr5yAqMVWz/jn6UGC7AYrBTEQUnhjMLji7yMFTZbtz\nUfHdv5B05VQkDZ4YkF3XjvAGKCKi8MU1Zhc8rX9tTykrzCUnAQCJl12PTtNfRPKQyR6H8tAL23r0\nmkaD3un5ZN4ARUQUvhjMLtguctB7eJTJUl6Cn95/EieXPQxL5XmITo/oDhd6/HoGnWBidrdGl0ek\nxBmQbKwLYFs/9CKoNFugVN05ZXu8AYqIKLxxKtuNCVnpyDty1u2acuXhApxZuxiWqjK0Hfkr6GIT\nvX4ts1XhyU/2YdcfHN/61LTkZ0mlGQadICXOgJIKs8PjXEREFF4YzB7YtP+0058pqwUl/1qG89tW\nwdCuK9pP+iOi03r4/FrFFWas3mVyGK6OdombrQpx0VHY9Qfvb48iIqLQw6lsD7jcTCU6mM8cR0Lm\nGHSc/oJfoWzj7Gy0s35wsxcRUeTgiNkDjnZnl+//N6I79oIhuSPSbpoH0Wv3t9JZ0DrbJc7NXkRE\nkaPVB7Ot5KappBJ6EViUavjf9Po127ljMjB7RQEUAKu5CsVfvIGy3RuQMGAs2o35jaahDDgP2rlj\nMppdK8nNXkREkaVVT2Xbl9wE0HA7lO1/bSU4ASBncDeYTx/Gybdmo2x3LtoMnoi2o+7VvE+ugta2\nS9y2Y5ulN4mIIo8oL64q1Ep2drbKy8trkddydQnF0IUbPSogkp5sxNOXC8ZcNxaIiUfbcQ/hwgFX\noLy61uMLLFzRi8CqFHdVExFFKBHJV0ple/JsRE9lNz1eZD8CnpCV7vGmKVNJJU7FXIRf3XM35s+f\nj/bt2zts3xNGg77ZVDRHvUREZBPRU9nOLqGw7Xp2t2mq6thenFrxe1hrqvDkZwdx7T2/awhlwPsC\nJAA4FU1ERC5F9IjZ3fGi4X3SHBYOUVYLzm1diXNb3kNUUgdYys6gMjoWs1cU4MEVBQDqKnLNv+GS\nhlD1ZOScEmfAhKx0BjERETkV0cHs7niRo8IhteeLULT2z6g+thfxF1+DtqPvhy4mDgBgvxpfXGHG\n3A8KAaAhaG1r2UlGA0qra2Gx/vwbBr1g/g2XaPXWiIgoQkV0MLs7XuRoRH12/auoOfk92o2bjfi+\nIyAupqnNFtVwxWLTkbCrTWdERETORHQwNx3JNg1I24ha1dZAWczQxcSj7ehfQ9WaYWjXxaPXcDZd\nzilrIiLyRUQHM+A6IOeOycBDf/8Mx1c9h6ik9ki7+XFEJXXwqn1W3SIiIi1F9K5sV5RSKC7YgBNL\nfgtVVoTE/qPRJSWu4YpFTxj0wqpbRESkqVYZzOfPn0dOTg5mzJiBKwZfjsP//RanPngSW+aNwDkX\nBUPsQzslzoDnb8vkdDUREWkq4qeyHamsrMTmzZvxxz/+EY899hj0ej2Aug1buvo62U2lJxuxZd6I\nlu4qERG1MhEZzPY7opPjDFAKKKmoRvThrXhu7n24NbsbDhw4gPj4+Ea/89iHexyGMi+KICKilhJx\nwbx6lwlzPyiE2VIXsMUVZljKi1G07kVUHfoPfmu2Qv/7B5pNQTuqEgbU1bFmdS4iImopEbfG/OQn\n+xpCGQAqD+3CiSUPoPrYXrQdfT+ieg9rKMlpz9mxJ6tSDGUiImoxETdiLq74efPW+Z2rUbzxTRja\ndUXqpKcRndYDgOMQdlcljIiIqCVE3IjZXkyXi5Ew4Dp0nP5CQygDjsN27pgMGA36Rt/j2jIREbU0\nv0bMIvI8gBsA1AA4COAupVSJFh3zlTq4BSXH/4eUq3+JmE4XIabTRY1+7ixs3VUJIyIiagn+TmXn\nAnhMKVUrIn8C8BiAR/3vlvfKy8sxa9YsHP3gTcSm94GqrYFERTd6Jt1N2LKMJhERBZtfwayU2mD3\n5TYAt/nXHd8UFhZi8uTJOHDgAH73u98h66Zf4cWNP3DkS0REYUfLzV8zAKzQsD2PlJWVYcSIEYiO\njkZubi5GjhwJALhtUI+W7goREZHf3AaziHwBoKODHz2ulPq4/pnHAdQCeNdFOzMBzASAbt26+dRZ\nRxISEvDee+9hwIABaN++vWbtEhERBYMoB5WuvGpAZDqA+wCMVEpVePI72dnZKi8vz6/XJSIiChci\nkq+UyvbkWX93ZV+Hus1eV3saykREROScv+eYXwWQCCBXRApE5HUN+kRERNRq+bsru5dWHSEiIqII\nr/xFREQUbhjMREREIYTBTEREFEIYzERERCGEwUxERBRCGMxEREQhhMFMREQUQhjMREREIYTBTERE\nFEIYzERERCHE79ulfHpRkdMAjmjYZCqAIg3bCya+l9ATKe8D4HsJVZHyXiLlfQDav5fuSqk0Tx4M\nSjBrTUTyPL1OK9TxvYSeSHkfAN9LqIqU9xIp7wMI7nvhVDYREVEIYTATERGFkEgJ5jeC3QEN8b2E\nnkh5HwDfS6iKlPcSKe8DCOJ7iYg1ZiIiokgRKSNmIiKiiBAxwSwiz4vIfhHZLSIfiUhysPvkKxGZ\nKCL7RMQqImG3w1FErhORAyLyvYjMC3Z/fCUi/xSRn0Rkb7D74i8R6Soim0Tku/p/t2YFu0++EJFY\nEdkhIoX17+PJYPfJXyKiF5FdIrI22H3xh4gcFpE9IlIgInnB7o8/RCRZRD6oz5TvROSKlnz9iAlm\nALkA+iql+gP4L4DHgtwff+wFcAuAzcHuiLdERA/grwDGArgYwBQRuTi4vfLZUgDXBbsTGqkFMEcp\n9QsAgwH8Jkz/uVQDGKGUygQwAMB1IjI4yH3y1ywA3wW7ExoZrpQaEAFHpl4G8LlSqg+ATLTwP5+I\nCWal1AalVG39l9sAdAlmf/yhlPpOKXUg2P3w0SAA3yulflBK1QBYDuCmIPfJJ0qpzQDOBrsfWlBK\n/aiU+k/9n0tR93806cHtlfdUnbL6Lw31f4XtRhkR6QJgPIB/BLsvVEdE2gC4CsCbAKCUqlFKlbRk\nHyImmJuYAeCzYHeilUoHcMzu6+MIwwCIZCLSA0AWgO3B7Ylv6qd+CwD8BCBXKRWW76PeSwAeAWAN\ndkc0oABsEJF8EZkZ7Hh90pUAAAIwSURBVM744QIApwEsqV9i+IeIxLdkB8IqmEXkCxHZ6+Cvm+ye\neRx103bvBq+n7nnyXsKUOPhe2I5oIo2IJABYBeBBpdT5YPfHF0opi1JqAOpmxQaJSN9g98kXInI9\ngJ+UUvnB7otGhiqlLkXdMtZvROSqYHfIR1EALgXwmlIqC0A5gBbdKxPVki/mL6XUKFc/F5HpAK4H\nMFKF+Dkwd+8ljB0H0NXu6y4ATgSpL2RHRAyoC+V3lVIfBrs//lJKlYjIV6jbBxCOG/SGArhRRMYB\niAXQRkTeUUpNDXK/fKKUOlH/vz+JyEeoW9YKu30yqPv/sON2MzEfoIWDOaxGzK6IyHUAHgVwo1Kq\nItj9acV2AugtIj1FJBrAZABrgtynVk9EBHVrZt8ppV4Idn98JSJpthMXImIEMArA/uD2yjdKqceU\nUl2UUj1Q99/JxnANZRGJF5FE258BjEZ4fliCUuokgGMiklH/rZEAvm3JPkRMMAN4FUAigNz67fqv\nB7tDvhKRm0XkOIArAKwTkfXB7pOn6jfg/R+A9ajbYLRSKbUvuL3yjYi8B2ArgAwROS4idwe7T34Y\nCuBOACPq//soqB+phZtOADaJyG7UfQjMVUqF9TGjCNEBwL9FpBDADgDrlFKfB7lP/ngAwLv1/54N\nAPBsS744K38RERGFkEgaMRMREYU9BjMREVEIYTATERGFEAYzERFRCGEwExERhRAGMxERUQhhMBMR\nEYUQBjMREVEI+X+zRP2yo2yNogAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a1e463438>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#还可以观察预测值与真值的散点图\n",
    "plt.figure(figsize=(8, 6))\n",
    "plt.scatter(y_train, lr_y_predict_train)\n",
    "plt.plot([-2, 6], [-2, 6], '--k')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RidgeCV coef [[ -2.52137696e-03   1.03017749e-02   4.87609050e-02   2.55319082e-02\n",
      "   -3.99286908e-03  -6.26965777e-03   9.69783427e-03   6.36817330e-03\n",
      "    7.89101052e-03   2.49759714e-02   6.74502321e-02   4.47311263e-02\n",
      "    4.39159257e-02   4.66573736e-03  -2.14412883e-02   3.95718463e-03\n",
      "   -1.22587188e-03   6.15237715e-02   9.07039375e-03   4.49591815e-02\n",
      "    6.15252850e-03  -1.31707479e-02  -2.79081002e-02   1.30026991e-02\n",
      "    1.07248338e-02   1.56513500e-02   2.96941818e-03   6.06379428e-03\n",
      "    1.50837424e-02  -6.45736867e-03   7.00222950e-04   2.32391826e-03\n",
      "    2.08747909e-02  -4.85795201e-02  -3.07918709e-02   1.34125085e-02\n",
      "    4.73059204e-02   5.65844480e-02   2.17250669e-02  -3.83741810e-03\n",
      "    6.72217020e-03  -1.13898200e-02  -9.91666540e-03   2.52860187e-02\n",
      "   -8.19567998e-03   2.13045071e-02   1.21652264e-02   1.79350722e-02\n",
      "    3.08491821e-03   5.50870399e-03   1.68417867e-02   3.25695530e-03\n",
      "    7.41202145e-03   3.46674440e-03   8.09052121e-03   4.78354039e-02\n",
      "    6.21448045e-05   1.72402574e-02   3.53711315e-03   2.17250669e-02\n",
      "    1.51446696e-02   1.45811776e-02   4.13326142e-03   1.68569587e-02\n",
      "    1.45897022e-02   2.34460914e-02   2.98720301e-03   2.16232917e-02\n",
      "    3.91253492e-02   6.04937094e-02   8.37495121e-03   2.96305859e-02\n",
      "    4.04506069e-02   7.44731050e-03   3.44407841e-02   5.23942959e-02\n",
      "    6.47405354e-03   5.96465485e-03   4.76558765e-03   9.21310162e-03\n",
      "    1.79105252e-02   4.48427089e-02  -1.50288576e-02   2.25945561e-02\n",
      "    1.12221128e-02  -5.94688592e-04   2.60868111e-02   6.07457092e-02\n",
      "   -1.50633760e-02   2.22333200e-02   3.35836890e-02  -5.52874015e-03\n",
      "    4.66573736e-03   4.66573736e-03   4.66573736e-03   2.25003073e-02\n",
      "    2.34361775e-02   1.17500451e-02   1.90347055e-02  -1.98165336e-03\n",
      "   -9.45159885e-03  -2.06464145e-02  -5.82732682e-03   0.00000000e+00\n",
      "   -1.03659906e-02   1.03659906e-02   0.00000000e+00  -1.11484380e-03\n",
      "   -1.45308271e-02   1.81946635e-02  -1.24864235e-02  -7.52805051e-03\n",
      "   -2.00891376e-02   4.15139341e-03   0.00000000e+00   4.23155620e-03\n",
      "    0.00000000e+00  -6.76395552e-03  -3.30145991e-03   8.98870158e-03\n",
      "    9.07095824e-03  -1.39756664e-02  -1.78904700e-02   3.62454846e-03\n",
      "    3.37428108e-03   0.00000000e+00   4.69228873e-03  -6.40496177e-03\n",
      "    2.00469174e-03   6.89772692e-03  -2.13343833e-03   2.77339891e-03\n",
      "    5.50027054e-03   7.94316617e-04   2.98026195e-03   3.43216003e-02\n",
      "   -3.22183865e-03   1.11451929e-02  -1.36682441e-02   5.40860459e-04\n",
      "    1.13969458e-02  -1.34326682e-02  -1.12467116e-02   1.16433877e-02\n",
      "   -3.74770926e-03  -1.41398209e-02   7.75530713e-03   0.00000000e+00\n",
      "   -1.42630326e-02  -1.72217184e-03   3.00343331e-03  -1.38862369e-02\n",
      "   -3.22183865e-03   2.83738226e-02  -9.50578003e-03   7.35026175e-04\n",
      "    2.13838198e-03   0.00000000e+00  -1.74134874e-02   7.88662585e-03\n",
      "   -4.63369792e-03   7.77488161e-03   1.09284780e-02  -1.16637076e-02\n",
      "    0.00000000e+00   7.62761638e-03  -3.80511178e-03  -2.04506714e-03\n",
      "    2.30263698e-03  -5.77578938e-04   0.00000000e+00  -4.14826711e-03\n",
      "   -1.12705588e-02   1.21627050e-02   9.95113475e-03   4.22974403e-03\n",
      "   -1.34311799e-02   0.00000000e+00   1.64596135e-02  -1.00913416e-02\n",
      "   -1.35032994e-02   2.99947705e-03   0.00000000e+00  -1.96889586e-02\n",
      "    7.41470535e-04  -2.79043099e-04   1.79284761e-02  -3.47835555e-03\n",
      "   -1.32547069e-03  -1.00913416e-02   0.00000000e+00  -1.71666894e-03\n",
      "    3.02487803e-03  -5.96578304e-03   3.41778514e-03   0.00000000e+00\n",
      "    1.37718265e-03   0.00000000e+00  -9.99295347e-03   7.55622341e-03\n",
      "   -8.79209285e-03  -2.56272479e-02  -2.89653974e-03   1.74632998e-02\n",
      "    3.00056515e-03   2.46395500e-03   0.00000000e+00   6.01025412e-03\n",
      "    1.93314934e-02  -1.10213964e-02  -1.00586024e-02   0.00000000e+00\n",
      "    1.39536232e-04   2.36601432e-02  -1.51719511e-02  -1.10997301e-02\n",
      "   -5.98177523e-03   0.00000000e+00  -6.30298790e-03   0.00000000e+00\n",
      "   -2.67133130e-02  -4.34573075e-03  -1.98165336e-03   1.23302844e-02\n",
      "   -2.12528390e-02   2.72054521e-03  -9.44769728e-03   9.63677750e-03\n",
      "    1.25433470e-02   5.93499364e-03   1.19306426e-02  -2.73943818e-03\n",
      "   -7.38451626e-03  -9.45159885e-03   0.00000000e+00  -3.60169289e-02\n",
      "   -4.90670303e-03   7.51973045e-03   2.08262942e-02  -1.48661671e-02\n",
      "    0.00000000e+00  -2.28737292e-02  -5.00162187e-03  -2.98720301e-03\n",
      "    2.07064189e-02   0.00000000e+00  -2.22158380e-03   4.95784866e-04\n",
      "    8.96112170e-04   4.17282355e-03  -2.58316004e-02   0.00000000e+00\n",
      "    5.46225806e-04  -5.39349194e-03  -6.51404533e-03  -7.52000594e-03\n",
      "    1.15632419e-02   4.80065813e-03  -3.28626966e-03   7.61670471e-03\n",
      "    5.45680693e-03  -9.45756074e-03  -4.37643019e-03   2.61549652e-03\n",
      "    0.00000000e+00  -1.45329776e-02   9.94619302e-05   9.21887056e-03\n",
      "    2.20141937e-02  -1.46752780e-03  -2.49776310e-02   2.94421373e-02\n",
      "   -1.78838166e-02  -3.10777763e-02   2.72930173e-03  -3.19472432e-02\n",
      "   -1.73709832e-02  -2.59935594e-02   1.73388602e-02  -1.99684287e-02\n",
      "    1.05829271e-02   8.15666564e-02  -1.16771347e-02  -9.50696011e-03\n",
      "   -4.35753471e-03  -4.60597646e-03   2.70247982e-02   6.75560178e-02\n",
      "   -9.87302542e-03   2.05922222e-02   0.00000000e+00  -3.51350521e-03\n",
      "    1.33973724e-02  -3.94677087e-04  -5.05103872e-04  -8.19842711e-03\n",
      "   -7.02554414e-03   2.25111706e-02   0.00000000e+00   1.11654090e-02\n",
      "   -1.73641553e-02   9.93034065e-04   1.26494101e-02   1.98712494e-03\n",
      "    1.97377084e-02   0.00000000e+00  -1.80713455e-02   1.05662476e-02\n",
      "   -5.93033894e-03  -1.87499370e-02   2.46802370e-03   2.16232917e-02\n",
      "    0.00000000e+00  -1.42098487e-02   1.25599822e-02   1.16850244e-02\n",
      "   -4.14817267e-03   1.01110488e-02  -4.93523969e-03   1.72177935e-02\n",
      "    1.04077208e-03  -1.05994522e-02   0.00000000e+00   0.00000000e+00\n",
      "    0.00000000e+00   0.00000000e+00   0.00000000e+00   0.00000000e+00\n",
      "    0.00000000e+00   0.00000000e+00   0.00000000e+00   0.00000000e+00\n",
      "    0.00000000e+00   0.00000000e+00   0.00000000e+00   0.00000000e+00\n",
      "    0.00000000e+00   0.00000000e+00   0.00000000e+00   0.00000000e+00\n",
      "    0.00000000e+00   0.00000000e+00   0.00000000e+00   0.00000000e+00]]\n",
      "RidgeCV alpha 100.0\n"
     ]
    }
   ],
   "source": [
    "from sklearn.linear_model import  RidgeCV\n",
    "\n",
    "alphas = [0.01, 0.1, 1, 10,20, 40, 80,100]\n",
    "reg = RidgeCV(alphas=alphas, store_cv_values=True)   \n",
    "reg.fit(X_train, y_train)  \n",
    "\n",
    "# 预测，下面计算score会自动调用predict\n",
    "reg_y_predict = reg.predict(X_test)\n",
    "reg_y_predict_train = reg.predict(X_train)\n",
    "\n",
    "#显示特征的回归系数\n",
    "print('RidgeCV coef',reg.coef_)\n",
    "\n",
    "#超参数\n",
    "print('RidgeCV alpha',reg.alpha_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The value of default measurement of RidgeCV on test is 0.885768420138\n",
      "The value of default measurement of RidgeCV on train is 0.934016370657\n",
      "Mean squared error: 0.11\n"
     ]
    }
   ],
   "source": [
    "# 使用RidgeCV模型自带的评估模块，并输出评估结果\n",
    "print ('The value of default measurement of RidgeCV on test is', reg.score(X_test, y_test))\n",
    "print ('The value of default measurement of RidgeCV on train is', reg.score(X_train, y_train))\n",
    "\n",
    "print(\"Mean squared error: %.2f\"\n",
    "      % mean_squared_error(reg_y_predict, y_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:1094: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n",
      "/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n",
      "  ConvergenceWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "LassoCV coef [ -0.00000000e+00   5.13846267e-03   4.43804368e-02   1.98308191e-02\n",
      "  -0.00000000e+00  -0.00000000e+00   0.00000000e+00  -0.00000000e+00\n",
      "   0.00000000e+00   0.00000000e+00   9.92013374e-02   4.88735857e-02\n",
      "   3.55381586e-02   1.84108386e-03  -0.00000000e+00   0.00000000e+00\n",
      "  -0.00000000e+00   5.72864598e-02   0.00000000e+00   7.47130793e-02\n",
      "   0.00000000e+00  -0.00000000e+00  -0.00000000e+00   0.00000000e+00\n",
      "   7.45891355e-03   1.18735127e-02   0.00000000e+00   0.00000000e+00\n",
      "   0.00000000e+00   0.00000000e+00  -0.00000000e+00   0.00000000e+00\n",
      "   1.73198278e-02  -3.20713194e-02  -3.40510787e-02   1.96455835e-02\n",
      "   4.69513182e-02   5.06288502e-02   3.92222725e-02   0.00000000e+00\n",
      "   0.00000000e+00   0.00000000e+00   0.00000000e+00   2.14340708e-02\n",
      "   0.00000000e+00   9.44695859e-03   9.47558539e-03   1.00218044e-02\n",
      "  -0.00000000e+00   0.00000000e+00   5.10745696e-03   0.00000000e+00\n",
      "   0.00000000e+00  -0.00000000e+00   0.00000000e+00   9.42194759e-02\n",
      "   0.00000000e+00   0.00000000e+00   0.00000000e+00   1.18533830e-02\n",
      "   0.00000000e+00   1.24120337e-02   0.00000000e+00   0.00000000e+00\n",
      "   6.26457385e-02   2.90352395e-02   0.00000000e+00   6.09973225e-02\n",
      "   0.00000000e+00   2.26172036e-01   0.00000000e+00   1.07597238e-02\n",
      "   0.00000000e+00   0.00000000e+00   9.12711604e-02   0.00000000e+00\n",
      "   0.00000000e+00   0.00000000e+00   0.00000000e+00   0.00000000e+00\n",
      "   0.00000000e+00   4.60523429e-02   0.00000000e+00   0.00000000e+00\n",
      "   0.00000000e+00   0.00000000e+00   0.00000000e+00   8.73758191e-02\n",
      "   0.00000000e+00   0.00000000e+00   4.07886907e-02   0.00000000e+00\n",
      "   2.40350425e-02   7.00294523e-15   0.00000000e+00   0.00000000e+00\n",
      "   0.00000000e+00   2.27192985e-03   4.21732402e-02   0.00000000e+00\n",
      "  -0.00000000e+00  -1.33777427e-02  -0.00000000e+00   0.00000000e+00\n",
      "  -7.00065562e-03   1.38269534e-16   0.00000000e+00  -0.00000000e+00\n",
      "  -2.71547427e-03   2.19481015e-02  -0.00000000e+00  -0.00000000e+00\n",
      "  -7.13220734e-03   0.00000000e+00   0.00000000e+00   0.00000000e+00\n",
      "   0.00000000e+00  -0.00000000e+00  -0.00000000e+00   0.00000000e+00\n",
      "   1.69172939e-03  -8.64531592e-03  -6.26566048e-03  -0.00000000e+00\n",
      "  -0.00000000e+00   0.00000000e+00   0.00000000e+00  -0.00000000e+00\n",
      "   0.00000000e+00   0.00000000e+00  -0.00000000e+00   0.00000000e+00\n",
      "  -0.00000000e+00   0.00000000e+00   0.00000000e+00   2.71288613e-02\n",
      "  -0.00000000e+00   0.00000000e+00  -1.08376060e-02   0.00000000e+00\n",
      "   2.15323730e-03  -3.85687084e-03  -0.00000000e+00   0.00000000e+00\n",
      "   0.00000000e+00  -0.00000000e+00   0.00000000e+00   0.00000000e+00\n",
      "  -0.00000000e+00  -0.00000000e+00   0.00000000e+00  -0.00000000e+00\n",
      "  -0.00000000e+00   2.67850669e-02  -1.08238503e-03   0.00000000e+00\n",
      "   0.00000000e+00   0.00000000e+00  -5.10943799e-03   0.00000000e+00\n",
      "  -0.00000000e+00   0.00000000e+00   0.00000000e+00  -0.00000000e+00\n",
      "   0.00000000e+00   0.00000000e+00  -0.00000000e+00  -0.00000000e+00\n",
      "  -0.00000000e+00   0.00000000e+00   0.00000000e+00  -0.00000000e+00\n",
      "  -8.41526393e-03   1.37683796e-02   0.00000000e+00   0.00000000e+00\n",
      "  -3.73023930e-03   0.00000000e+00   1.86935368e-02  -0.00000000e+00\n",
      "  -0.00000000e+00   0.00000000e+00   0.00000000e+00  -1.26187281e-02\n",
      "   0.00000000e+00  -0.00000000e+00   7.11895947e-03  -0.00000000e+00\n",
      "  -0.00000000e+00  -0.00000000e+00   0.00000000e+00  -0.00000000e+00\n",
      "   0.00000000e+00  -2.00583870e-03  -0.00000000e+00   0.00000000e+00\n",
      "   0.00000000e+00   0.00000000e+00  -0.00000000e+00   0.00000000e+00\n",
      "  -0.00000000e+00  -5.44529595e-03   0.00000000e+00   0.00000000e+00\n",
      "  -0.00000000e+00  -0.00000000e+00   0.00000000e+00   0.00000000e+00\n",
      "   1.72156952e-02  -0.00000000e+00  -0.00000000e+00   0.00000000e+00\n",
      "   0.00000000e+00   2.20711331e-02  -0.00000000e+00  -5.83587631e-04\n",
      "  -0.00000000e+00   0.00000000e+00  -0.00000000e+00   0.00000000e+00\n",
      "  -7.66441469e-03  -0.00000000e+00   0.00000000e+00   0.00000000e+00\n",
      "  -9.71340627e-03   0.00000000e+00   0.00000000e+00  -0.00000000e+00\n",
      "   0.00000000e+00   0.00000000e+00   0.00000000e+00  -0.00000000e+00\n",
      "  -0.00000000e+00  -0.00000000e+00   0.00000000e+00  -3.82879473e-02\n",
      "   0.00000000e+00   0.00000000e+00   0.00000000e+00  -1.73280042e-02\n",
      "   0.00000000e+00  -1.30469830e-02  -0.00000000e+00  -0.00000000e+00\n",
      "   2.31732319e-02   0.00000000e+00  -0.00000000e+00   0.00000000e+00\n",
      "   0.00000000e+00   0.00000000e+00  -1.50788620e-02   0.00000000e+00\n",
      "  -0.00000000e+00  -0.00000000e+00  -0.00000000e+00  -0.00000000e+00\n",
      "   0.00000000e+00   0.00000000e+00  -0.00000000e+00   0.00000000e+00\n",
      "   0.00000000e+00  -7.20996115e-04  -0.00000000e+00   0.00000000e+00\n",
      "   0.00000000e+00  -0.00000000e+00   0.00000000e+00   0.00000000e+00\n",
      "   1.46775842e-02   0.00000000e+00  -0.00000000e+00   3.58767728e-02\n",
      "  -0.00000000e+00  -0.00000000e+00  -0.00000000e+00  -2.02119290e-02\n",
      "  -1.09106259e-03  -0.00000000e+00   7.66742464e-03  -2.57670339e-03\n",
      "   1.33077452e-02   9.67073438e-02  -0.00000000e+00  -0.00000000e+00\n",
      "  -0.00000000e+00   0.00000000e+00   2.26401601e-02   7.43495678e-02\n",
      "   0.00000000e+00   1.30470209e-02   0.00000000e+00  -0.00000000e+00\n",
      "   5.44133742e-03   0.00000000e+00  -0.00000000e+00  -0.00000000e+00\n",
      "   0.00000000e+00   1.05614566e-02   0.00000000e+00   0.00000000e+00\n",
      "  -2.12033224e-02  -0.00000000e+00   0.00000000e+00   0.00000000e+00\n",
      "   0.00000000e+00   0.00000000e+00  -1.71533054e-02   0.00000000e+00\n",
      "   0.00000000e+00  -1.33602099e-02   0.00000000e+00   3.65345185e-03\n",
      "   0.00000000e+00  -0.00000000e+00   4.36496433e-03   8.29397524e-03\n",
      "  -0.00000000e+00   0.00000000e+00  -0.00000000e+00   0.00000000e+00\n",
      "   0.00000000e+00  -0.00000000e+00   0.00000000e+00   0.00000000e+00\n",
      "   0.00000000e+00   0.00000000e+00   0.00000000e+00   0.00000000e+00\n",
      "   0.00000000e+00   0.00000000e+00   0.00000000e+00   0.00000000e+00\n",
      "   0.00000000e+00   0.00000000e+00   0.00000000e+00   0.00000000e+00\n",
      "   0.00000000e+00   0.00000000e+00   0.00000000e+00   0.00000000e+00\n",
      "   0.00000000e+00   0.00000000e+00   0.00000000e+00   0.00000000e+00]\n",
      "LassoCV alpha 0.01\n"
     ]
    }
   ],
   "source": [
    "from sklearn.linear_model import LassoCV\n",
    "\n",
    "alphas = [0.01, 0.1, 1, 10,100]\n",
    "\n",
    "lasso = LassoCV(alphas=alphas)   \n",
    "lasso.fit(X_train, y_train) \n",
    "\n",
    "# 预测，下面计算score会自动调用predict\n",
    "lasso_y_predict = lasso.predict(X_test)\n",
    "lasso_y_predict_train = lasso.predict(X_train)\n",
    "\n",
    "#显示特征的回归系数\n",
    "print('LassoCV coef',lasso.coef_)\n",
    "\n",
    "#超参数\n",
    "print('LassoCV alpha',lasso.alpha_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The value of default measurement of LassoCV on test is 0.89130201126\n",
      "The value of default measurement of LassoCV on train is 0.924829824323\n",
      "Mean squared error: 0.11\n"
     ]
    }
   ],
   "source": [
    "# 使用LassoCV模型自带的评估模块，并输出评估结果\n",
    "print ('The value of default measurement of LassoCV on test is', lasso.score(X_test, y_test))\n",
    "print ('The value of default measurement of LassoCV on train is', lasso.score(X_train, y_train))\n",
    "\n",
    "print(\"Mean squared error: %.2f\"\n",
    "      % mean_squared_error(reg_y_predict, y_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#综上，选用LassoCV作为模型"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 预测测试集"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "target['SalePrice'] = lasso.predict(target.drop('Id',axis=1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th>Id</th>\n",
       "      <th>LotFrontage</th>\n",
       "      <th>LotArea</th>\n",
       "      <th>Street</th>\n",
       "      <th>Alley</th>\n",
       "      <th>LotShape</th>\n",
       "      <th>Utilities</th>\n",
       "      <th>LandSlope</th>\n",
       "      <th>OverallQual</th>\n",
       "      <th>...</th>\n",
       "      <th>SimplGarageScore_nan</th>\n",
       "      <th>SimplHeatingQC_nan</th>\n",
       "      <th>SimplKitchenQual_nan</th>\n",
       "      <th>SimplKitchenScore_nan</th>\n",
       "      <th>SimplOverallCond_nan</th>\n",
       "      <th>SimplOverallGrade_nan</th>\n",
       "      <th>SimplOverallQual_nan</th>\n",
       "      <th>SimplPoolQC_nan</th>\n",
       "      <th>SimplPoolScore_nan</th>\n",
       "      <th>SalePrice</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
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       "      <td>1461</td>\n",
       "      <td>0.670403</td>\n",
       "      <td>0.119019</td>\n",
       "      <td>0.064327</td>\n",
       "      <td>-0.243378</td>\n",
       "      <td>0.700717</td>\n",
       "      <td>0.026216</td>\n",
       "      <td>0.226042</td>\n",
       "      <td>-0.694099</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>-0.644066</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>1462</td>\n",
       "      <td>0.699931</td>\n",
       "      <td>0.387346</td>\n",
       "      <td>0.064327</td>\n",
       "      <td>-0.243378</td>\n",
       "      <td>-1.028509</td>\n",
       "      <td>0.026216</td>\n",
       "      <td>0.226042</td>\n",
       "      <td>-0.694099</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>-0.314729</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>1463</td>\n",
       "      <td>0.493235</td>\n",
       "      <td>0.343014</td>\n",
       "      <td>0.064327</td>\n",
       "      <td>-0.243378</td>\n",
       "      <td>-1.028509</td>\n",
       "      <td>0.026216</td>\n",
       "      <td>0.226042</td>\n",
       "      <td>-0.694099</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.078565</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>1464</td>\n",
       "      <td>0.611347</td>\n",
       "      <td>-0.047760</td>\n",
       "      <td>0.064327</td>\n",
       "      <td>-0.243378</td>\n",
       "      <td>-1.028509</td>\n",
       "      <td>0.026216</td>\n",
       "      <td>0.226042</td>\n",
       "      <td>-0.694099</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.100071</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>1465</td>\n",
       "      <td>-0.422133</td>\n",
       "      <td>-0.552255</td>\n",
       "      <td>0.064327</td>\n",
       "      <td>-0.243378</td>\n",
       "      <td>-1.028509</td>\n",
       "      <td>0.026216</td>\n",
       "      <td>0.226042</td>\n",
       "      <td>1.253123</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.012885</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 346 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   Unnamed: 0    Id  LotFrontage   LotArea    Street     Alley  LotShape  \\\n",
       "0           0  1461     0.670403  0.119019  0.064327 -0.243378  0.700717   \n",
       "1           1  1462     0.699931  0.387346  0.064327 -0.243378 -1.028509   \n",
       "2           2  1463     0.493235  0.343014  0.064327 -0.243378 -1.028509   \n",
       "3           3  1464     0.611347 -0.047760  0.064327 -0.243378 -1.028509   \n",
       "4           4  1465    -0.422133 -0.552255  0.064327 -0.243378 -1.028509   \n",
       "\n",
       "   Utilities  LandSlope  OverallQual    ...      SimplGarageScore_nan  \\\n",
       "0   0.026216   0.226042    -0.694099    ...                         1   \n",
       "1   0.026216   0.226042    -0.694099    ...                         1   \n",
       "2   0.026216   0.226042    -0.694099    ...                         1   \n",
       "3   0.026216   0.226042    -0.694099    ...                         1   \n",
       "4   0.026216   0.226042     1.253123    ...                         1   \n",
       "\n",
       "   SimplHeatingQC_nan  SimplKitchenQual_nan  SimplKitchenScore_nan  \\\n",
       "0                   1                     1                      1   \n",
       "1                   1                     1                      1   \n",
       "2                   1                     1                      1   \n",
       "3                   1                     1                      1   \n",
       "4                   1                     1                      1   \n",
       "\n",
       "   SimplOverallCond_nan  SimplOverallGrade_nan  SimplOverallQual_nan  \\\n",
       "0                     1                      1                     1   \n",
       "1                     1                      1                     1   \n",
       "2                     1                      1                     1   \n",
       "3                     1                      1                     1   \n",
       "4                     1                      1                     1   \n",
       "\n",
       "   SimplPoolQC_nan  SimplPoolScore_nan  SalePrice  \n",
       "0                1                   1  -0.644066  \n",
       "1                1                   1  -0.314729  \n",
       "2                1                   1   0.078565  \n",
       "3                1                   1   0.100071  \n",
       "4                1                   1   0.012885  \n",
       "\n",
       "[5 rows x 346 columns]"
      ]
     },
     "execution_count": 106,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "target.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "target.to_csv('AmesHouse.csv')"
   ]
  }
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